Theory Wiki - User contributions [en]

E-Learning Design Principles and Methods 2017

2017-07-27T16:59:36Z

Jobodnar: /* Class URLs */ http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017 http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017 ]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-9 to 11-23=====

*'''11-9''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-14''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-28

*'''11-16''' ''Finish discussion of experimentation''

*'''11-21''' ''Flex topic; Presentation & Report Preparation''

*'''11-23''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-28 to 12-7=====

*'''11-28''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''11-30''' ''Project Presentations''
*'''12-5''' ''Project Presentations''
*'''12-7''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T16:58:18Z

Jobodnar: /* Class URLs */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-9 to 11-23=====

*'''11-9''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-14''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-28

*'''11-16''' ''Finish discussion of experimentation''

*'''11-21''' ''Flex topic; Presentation & Report Preparation''

*'''11-23''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-28 to 12-7=====

*'''11-28''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''11-30''' ''Project Presentations''
*'''12-5''' ''Project Presentations''
*'''12-7''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T16:57:53Z

Jobodnar: /* Class URLs */ http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017
http://www.learnlab.org/research/wiki/index.php/E-Learning_Design_Principles_and_Methods_2017]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-9 to 11-23=====

*'''11-9''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-14''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-28

*'''11-16''' ''Finish discussion of experimentation''

*'''11-21''' ''Flex topic; Presentation & Report Preparation''

*'''11-23''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-28 to 12-7=====

*'''11-28''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''11-30''' ''Project Presentations''
*'''12-5''' ''Project Presentations''
*'''12-7''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:59:06Z

Jobodnar: /* Final & Project Presentations 11-28 to 12-7 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-9 to 11-23=====

*'''11-9''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-14''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-28

*'''11-16''' ''Finish discussion of experimentation''

*'''11-21''' ''Flex topic; Presentation & Report Preparation''

*'''11-23''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-28 to 12-7=====

*'''11-28''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''11-30''' ''Project Presentations''
*'''12-5''' ''Project Presentations''
*'''12-7''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:57:06Z

Jobodnar: /* Putting it together & evaluation 11-9 to 11-23 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-9 to 11-23=====

*'''11-9''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-14''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-28

*'''11-16''' ''Finish discussion of experimentation''

*'''11-21''' ''Flex topic; Presentation & Report Preparation''

*'''11-23''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:55:27Z

Jobodnar: /* Multimedia Principles 10-26 to 11-7 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-26 to 11-7=====
*'''10-26''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/30 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-16

*'''10-31''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-2''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-7''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:52:48Z

Jobodnar: /* Learning By Doing Principles 10-4 to 10-25 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-3 to 10-24 =====
*'''10-3''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-5''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-10''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-12''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-17''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-19''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-24''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:50:29Z

Jobodnar: /* Cognitive Task Analysis (CTA) 9-19 to 9-28 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-19 to 9-28=====
*'''9-19''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-21''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-26''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-28''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 12

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:48:55Z

Jobodnar: /* Instructional Goals and Assessment 9-5 to 9-14 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-5 to 9-14=====

*'''9-5''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-7''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-12''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-14''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 28

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:47:00Z

Jobodnar: /* E-Learning Introduction 8-29 to 8-31 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2017

2017-07-27T15:43:41Z

Jobodnar: /* Class Schedule in Brief */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Administrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-29 to 8-31
**Aug 29 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Aug 31 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-5 to 9-14
**Sept 5 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 7 Writing assessments to meet goals; Evidence-centered design
**Sept 12 Why data toward goal setting improves design
**Sept 14 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-28
**Sept 19 Empirical CTA: Structured Interviews
**Sept 21 Think Alouds & Rational CTA
**Sept 26 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 28 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-3 to 10-24
**Oct 3 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 5
**Oct 10 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 12 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 17 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 19 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 24 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-26 to 11-23
**Oct 26 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Oct 31
**Nov 2 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 7 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 9 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 14 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 16 In vivo experimentation; A/B Testing
**Nov 21 Flex topic; Presentation & Report Preparation
**Nov 23 Thanksgiving, no class
*Final & Project Presentations 11-28 to 12-7
**Nov 28 Project Presentations
**Nov 30 Project Presentations
**Dec 5 Project Presentations
**Dec 7 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-30 to 9-1=====

*'''8-30''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''9-1''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2016

2017-07-25T13:54:09Z

Jobodnar: /* E-Learning Introduction 8-30 to 9-1 */

====Course Details====
Course number: 05-823

Semester: Fall 2016

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Admininstrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-30 to 9-1
**Aug 30 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 1 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-6 to 9-15
**Sept 6 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 8 Writing assessments to meet goals; Evidence-centered design
**Sept 13 Why data toward goal setting improves design
**Sept 15 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-29
**Sept 20 Empirical CTA: Structured Interviews
**Sept 22 Think Alouds & Rational CTA
**Sept 27 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 29 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-4 to 10-25
**Oct 4 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 6
**Oct 11 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 13 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 18 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 20 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 25 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-7 to 11-24
**Oct 27 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Nov 1
**Nov 3 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 8 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 10 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 15 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 17 In vivo experimentation; A/B Testing
**Nov 22 Flex topic; Presentation & Report Preparation
**Nov 24 Thanksgiving, no class
*Final & Project Presentations 11-29 to 12-8
**Nov 29 Project Presentations
**Dec 1 Project Presentations
**Dec 6 Project Presentations
**Dec 8 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-30 to 9-1=====

*'''8-30''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''9-1''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2016

2017-07-25T13:51:05Z

Jobodnar: /* Course Details */

====Course Details====
Course number: 05-823

Semester: Fall 2016

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Admininstrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-30 to 9-1
**Aug 30 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 1 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-6 to 9-15
**Sept 6 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 8 Writing assessments to meet goals; Evidence-centered design
**Sept 13 Why data toward goal setting improves design
**Sept 15 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-29
**Sept 20 Empirical CTA: Structured Interviews
**Sept 22 Think Alouds & Rational CTA
**Sept 27 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 29 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-4 to 10-25
**Oct 4 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 6
**Oct 11 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 13 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 18 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 20 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 25 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-7 to 11-24
**Oct 27 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Nov 1
**Nov 3 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 8 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 10 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 15 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 17 In vivo experimentation; A/B Testing
**Nov 22 Flex topic; Presentation & Report Preparation
**Nov 24 Thanksgiving, no class
*Final & Project Presentations 11-29 to 12-8
**Nov 29 Project Presentations
**Dec 1 Project Presentations
**Dec 6 Project Presentations
**Dec 8 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2016

2017-07-21T16:19:07Z

Jobodnar: /* E-Learning Introduction 8-29 to 8-31 */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Admininstrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-30 to 9-1
**Aug 30 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 1 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-6 to 9-15
**Sept 6 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 8 Writing assessments to meet goals; Evidence-centered design
**Sept 13 Why data toward goal setting improves design
**Sept 15 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-29
**Sept 20 Empirical CTA: Structured Interviews
**Sept 22 Think Alouds & Rational CTA
**Sept 27 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 29 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-4 to 10-25
**Oct 4 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 6
**Oct 11 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 13 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 18 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 20 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 25 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-7 to 11-24
**Oct 27 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Nov 1
**Nov 3 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 8 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 10 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 15 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 17 In vivo experimentation; A/B Testing
**Nov 22 Flex topic; Presentation & Report Preparation
**Nov 24 Thanksgiving, no class
*Final & Project Presentations 11-29 to 12-8
**Nov 29 Project Presentations
**Dec 1 Project Presentations
**Dec 6 Project Presentations
**Dec 8 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-29 to 8-31=====

*'''8-29''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 4. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-14
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''8-31''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 8-31"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

E-Learning Design Principles and Methods 2016

2017-07-21T16:04:02Z

Jobodnar: /* Course Details */

====Course Details====
Course number: 05-823

Semester: Fall 2017

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 4301

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

Teaching assistant: Mimi McLaughlin Email: mimim@cs.cmu.edu

Admininstrative assistant: Jo Bodnar Email: jobodnar@cs.cmu.edu

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 4th edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
For the syllabus go to [http://www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no/ www.learnlab.org/research/wiki/index.php?title=E-Learning_Design_Principles_and_Methods_2016&redirect=no]

For quizzes and reading reports go to [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 8am'' on the day of class. Quizzes can be taken as many times as you want and your score will be the last attempt before 8am.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy (updated 8/30/16) ====

Given that class discussion is a major part of the course, you should only use laptops, cell phones, and smart phones when you are directed to do so or within the common note taking Google page that I will provide. Deviation form this policy will result in a reduction in your participation grade. You will often need or want a laptop or smart device. You will definitely need one during testing days (marked as such on the schedule).

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 35% Final Project [[Media:E-learning-project-assignment-2016.docx|Project assignment]] '''(Submit project steps preferably as a Google document (provide access by sharing with the instructor and TA), but a Word document is OK. Do not submit a pdf.)'''
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 15% Final Exam
* 15% Class participation, including reading summary presentations

====Class Schedule in Brief====
*E-Learning Introduction 8-30 to 9-1
**Aug 30 Overview; Examples Assignment; Project; 1.E-learning (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 1 2.How People Learn; Instructional complexities; Project topic brainstorming
*Instructional Goals and Assessment 9-6 to 9-15
**Sept 6 Determining instructional goals; KLI KCs; Bloom's taxonomy
**Sept 8 Writing assessments to meet goals; Evidence-centered design
**Sept 13 Why data toward goal setting improves design
**Sept 15 Online assessment; Practice e-assessment implementation
*Cognitive Task Analysis (CTA) 9-20 to 9-29
**Sept 20 Empirical CTA: Structured Interviews
**Sept 22 Think Alouds & Rational CTA
**Sept 27 Quantitative Cognitive Task Analysis: Difficulty Factors Assessment
**Sept 29 Quantitative CTA via Data Mining
*Learning By Doing Principles 10-4 to 10-25
**Oct 4 3.Evidence-based practice; KLI Learning & Instructional Events
**Oct 6
**Oct 11 12.Does Practice Make Perfect; 14.Who’s in Control?
**Oct 13 15.E-Learning to Build Problem Solving Skill; 16.Simulations and Games
**Oct 18 10.Segmenting and Pretraining; 11.Leveraging Examples in E-Learning
**Oct 20 KLI & Selecting appropriate instructional principles; Midterm review
**Oct 25 Midterm exam '''Bring laptop to class'''
*Multimedia Principles 10-7 to 11-24
**Oct 27 [Guest topic: Options CSCL, Cognitive Mastery, Hint Factory, CTAT?]
**Nov 1
**Nov 3 4.Multimedia Principle; 5.Contiguity Principle; Practice applying
**Nov 8 6.Modality Principle & 7.Redundancy Principle; Practice applying
**Nov 10 8.Coherence Principle & 9.Personalization Principle; Practice applying
**Nov 15 17.Applying the Guidelines; KLI Review; Peer review of instructional design [too much on one day?]
**Nov 17 In vivo experimentation; A/B Testing
**Nov 22 Flex topic; Presentation & Report Preparation
**Nov 24 Thanksgiving, no class
*Final & Project Presentations 11-29 to 12-8
**Nov 29 Project Presentations
**Dec 1 Project Presentations
**Dec 6 Project Presentations
**Dec 8 Final Exam '''Bring laptop to class'''
*Final Project due Dec 12
*If needed: Final Exam Make-up -TBD

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- parts will evolve as I get a better sense of your needs.

=====E-Learning Introduction 8-30 to 9-1=====

*'''8-30''' ''Course Objectives & Course Project; The boom in e-learning!''
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (20 pages). [[Media:Ch1-4th_edition.pdf|This chapter is here (click to get)]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard Try to do this quiz before Tues class but it must be completed before Thurs class.
***Slides for this chapter are [[Media:Chpt1-e-learning-promises-pitfalls-2015.pptx|here]].
**Class activity: Introduce your background and interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment2016.docx|Examples assignment]] is due next Mon, Sept 5. Please submit on blackboard.
**Assignment: [[Media:E-learning-project-assignment-2016.docx|Project]] step 1 is due in 16 days on Thursday, 9-15
**For next time:
***BRING two screen shots of an e-learning example to ''next'' class
***Review project step 1 and come with a preliminary project idea.
***a) Do the readings & b) associated flipped homework (See next date for reading assignment)

*'''9-1''' ''How People Learn; Instructional complexities; Project topic brainstorming''
**Read Ch2. How Do People Learn from E-Courses (20 pages) [[Media:Ch2-4th_edition.pdf|You can get the chapter here this '''last''' time!]] ''Please order the book now if you have not!''
**Read Koedinger et al. (2013) paper [[Media:Koedinger-Science-2013.pdf|Instructional complexities]]
***Do the quiz for Chapter 2 (Quiz 2).
***On Blackboard do a "Discussion Board" post for Instructional complexities paper within the forum titled "Instructional Complexity Reading Posts for 9-1"
**Class activity: Project idea discussion
***Be ready to discuss some of your ''preliminary project ideas'' -- enter these in Google Doc of class notes
**For next time:
***a) Do the readings & b) associated flipped homework

=====Instructional Goals and Assessment 9-6 to 9-15=====

*'''9-6''' ''Determining instructional goals; KLI KCs; Bloom's taxonomy''
**Reading: [[Media:CogInstCarver12.pdf|Carver paper]]
**Reading: KLI sections 1-3 [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] (we will discuss other sections later)
**Additional Reading: [[Media:Krathwohl_Bloom's taxonomy revised.pdf|Bloom's taxonomy revised]]
***Do Discussion Board post on Carver paper.
***Do quiz for the KLI reading.
**Class activity: Review Project ideas and step 1 write-up requirements
**Class activity: Review of e-learning examples
***BRING a print-out of your e-learning examples to class
***We will find examples of promises & pitfalls, knowledge component types, etc.

*'''9-8''' ''Writing assessments to meet goals''
**Reading: [https://www.cmu.edu/teaching/assessment/index.html Assessment design--Eberly Center web pages on Assess Teaching and Learning] Read: Basics, Prior Knowledge, Assessing Learning, Examples and Tools (Skip: Assessing Teaching, Assessing Programs, History at Carnegie Mellon)
***Do Discussion Board post on reading.
**Class activity: Goal setting & designing assessments that provide evidence of goal achievement

*'''9-13''' ''Why data toward goal setting improves design''
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf | Feldon paper]]
**At least skim: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Reading: [[Media:Contextual Inquiry Principles.pdf|Contextual inquiry principles]]
***Do quiz for the Feldon reading.
***Do ONE discussion board post.
**Class activity: Interviewing practice

*'''9-15''' ''Online assessment; Practice e-assessment implementation''
**Reading: Read the documentation for two online assessment authoring tools of your choosing
***Discussion board posts on pros and cons of the tools you read about
**Class activity: BRING YOUR LAPTOP and be prepared to use an online assessment development tool
**DUE: P1: Context & Initial Resources '''(Submit all project steps as a shared google doc or in Blackboard as a Word doc. Do not submit a pdf.)'''
**Assignment: P2 is due Sept 29

=====Cognitive Task Analysis (CTA) 9-20 to 9-29=====
*'''9-20''' ''Empirical CTA: Structured Interviews''
**Reading: [[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Reading: [[Media:Contextual Inquiry Practice.pdf|Contextual inquiry in practice]]
**Reading: [[Media:Koh .pdf|CTA interview strategies]] (a paper by a former METALS student!)
**Other Readings: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]; [[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];
***Do posts for readings.

*'''9-22''' ''Think Alouds & Rational CTA''
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]] -- [[Media:L05_Think_Aloud_2014-Lovett-Gomoll-for_syllabus.pptx|Slides]]
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]] -- [[Media:L06-ELDP-Rational-CTA-2014-b-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''9-27''' ''Quantitative Cognitive Task Analysis: Difficulty Factors Assessment''
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]] -- [[Media:L08-ELDP-CTA-DFA-2014-Heffernan_forsyllabus.pptx|Slides]]
**Come with an initial draft of project step 2.
***Do quiz for Heffernan paper.

*'''9-29''' ''Quantitative CTA via Data Mining; CTA to improve instructional design''
**Class activity: Peer review of P2
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
***Do quiz for e-learning data to improvement paper
**DUE: P2: Identifying Goals & Online Assessment Creation
**Assignment: P3 is due Oct 13

=====Learning By Doing Principles 10-4 to 10-25 =====
*'''10-4''' ''Evidence-based practice; KLI Learning & Instructional Events''
**Reading: Clark & Mayer book Ch3.Evidence-based practice (18 pages) [[Media:L03-ELDP-evidence-KLI-2014.pptx|Slides]]
**Reading: KLI paper sections 4-5 (12 pages)
*** Do quizzes for the readings.

*'''10-6''' ''Practice, practice, practice''
**Reading: [[Media:Make_It_Stick_Ch1-2.pdf|Make It Stick Ch 1 and Ch 2 (45 pages) ]]
**Recommended reading: [[Media:Visible_Learning_Ch_9.pdf|Visible Learning Ch 9 (38 pages) ]]
** Additional Reading: [[Media:Scheines_2005.pdf|Scheines paper (20 pages) ]]
**Class activity: Work on P3. How will you collect data?
*** Do posts for the readings.

*'''10-11''' ''Does Practice Make Perfect; Who’s in Control?''
**Reading: 12.Does Practice Make Perfect (28 pages) [[Media:L18-ELDP-Practice-2014-for_syllabus.pptx|Slides]]
**Reading: 14.Who’s in Control? (30 pages) [[Media:Principles_e-learning_14.pdf |Slides]]
***Do quizzes for the readings.

*'''10-13''' ''E-Learning to Build Problem Solving Skill; Simulations and Games''
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages) [[Media:L22-ELDP-ThinkingSkills-2014-for_syllabus.pptx|Slides]]
**Reading: 16.Simulations and Games (32 pages) [[Media:L23-ELDP-GamesSimulations-2014_for_syllabus.pptx|Slides]]
***Do quizzes for the readings.
**DUE: P3: Cognitive Task Analysis & Cognitive Model
**Assignment: P4 is due Oct 31

*'''10-18''' ''Segmenting and Pretraining; Leveraging Examples in E-Learning''
**Reading: 10.Segmenting and Pretraining (18 pages) [[Media:L16-ELDP-Segmenting-Pretraining-2014-for syllabus.pptx|Slides]]
**Reading: 11.Leveraging Examples in E-Learning (28 pages) [[Media:L17-ELDP-Worked-Examples-2014-for_syllabus.pptx|Slides]]
***Do quizzes for the readings.

*'''10-20''' ''Midterm review''
**Do review quiz and bring questions to class
**No new post or quiz.
**Optional Readings:
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]

*'''10-25''' ''Midterm exam'' '''Bring laptop to class'''

=====Multimedia Principles 10-27 to 11-8=====
*'''10-27''' ''Optional topic''
**E-Learning in Industry
**Work on project
**DUE Mon, 10/31 by 12 noon: P4: Initial Instructional Design
**Assignment: P5 is due 11-17

*'''11-1''' ''Multimedia Principle; Contiguity Principle; Practice applying''
**Reading: 4.Multimedia Principle (24 pages) [[Media:L07-ELDP-Multimedia-2014_for_syllabus.pptx|Slides]]
**Reading: 5.Contiguity Principle (24 pages) [[Media:Contiguity2014.pptx|Slides]]
*** Do quizzes for the readings.
**Optional readings & slides about the multimedia principle:
*** Systematic Thinking Fostered by Illustrations in Scientific Text [[Media:Mayer_89.pdf|Paper]] [[Media:Mayer_89.pptx|Slides]]
*** Multimedia-Supported Metaphors for Meaning Making in Mathematics [[Media:Moreno & Mayer_99.pdf|Paper]] [[Media:Morena-Mayer-1999.pptx|Slides]]
**Optional readings & slides about the Contiguity principle:
***Cognitive Principles of Multimedia Learning: The Role of Modality and Contiguity [[Media:Moreno Mayer Modality-Contiguity.pdf|Paper]] [[Media:MorenoMayer1999-1.pptx|Slides]]
***Why Some Material is Difficult to Learn [[Media:Sweller Chandler Why Some Material is Difficult to Learn.pdf |Paper]] [[Media:SwellerChandler1994.pptx|Slides]]

*'''11-3''' ''Modality Principle & Redundancy Principle; Practice applying''
**Reading: 6.Modality Principle (18 pages) [[Media:Modality-2014-1.pptx |Slides]]
**Reading: 7.Redundancy Principle (18 pages) [[Media:L12-ELDP-Redundancy&eData-2014.pptx|Slides]]
***Do quizzes for the readings.

*'''11-8''' ''Coherence Principle & Personalization Principle; Practice applying''
**Reading: 8.Coherence Principle (28 pages) [[Media:L12-ELDP-Coherence-2014.pptx|Slides]]
**Reading: 9.Personalization Principle (26 pages) [[Media:L14-ELDP-Personalization-2014.pptx|Slides]]
***Do quizzes for the readings.

=====Putting it together & evaluation 11-10 to 11-24=====

*'''11-10''' ''Applying the Guidelines; KLI & Selecting appropriate instructional principles''
**Reading: 17.Applying the Guidelines (24 pages) [[Media:L25-ELDPCh17-Guidelines-KLIreview-2014.pptx|Slides]]
**Reading: KLI sections 6-7 [[Media:L17-ELDP-KLI-selecting-principles-for syllabus.pptx|Slides]]
***Do posts for the reading.
** Time permitting: Peer review of instructional design

*'''11-15''' ''In vivo experimentation; A/B Testing''
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-29

*'''11-17''' ''Finish discussion of experimentation''

*'''11-22''' ''Flex topic; Presentation & Report Preparation''

*'''11-24''' ''Thanksgiving, no class''

=====Final & Project Presentations 11-29 to 12-18=====

*'''11-29''' ''Project Presentations''
** DUE: P6: Experimental Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*'''12-1''' ''Project Presentations''
*'''12-6''' ''Project Presentations''
*'''12-8''' Final Exam '''Bring laptop to class'''
**If needed: Final Exam Make-up - TBD

====='''Final Project Due 12-12'''=====

Educational Research Methods 2017

2016-10-24T16:14:34Z

Jobodnar: /* Class Schedule in Brief */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2, 4 (TRTR)
* Wrap-up: May 9 (T)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-11
**Before class on 4-11, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-13
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-18
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

*4-20 NO CLASS - Spring Carnival

=====Experimental Research Methods (Koedinger)=====
*4-25 Continuation of Causal Inference
*4-27
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*5-2
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-4
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 11.

Educational Research Methods 2017

2016-10-24T16:11:49Z

Jobodnar: /* Class Schedule in Brief */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 9 (T)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-11
**Before class on 4-11, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-13
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-18
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

*4-20 NO CLASS - Spring Carnival

=====Experimental Research Methods (Koedinger)=====
*4-25 Continuation of Causal Inference
*4-27
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*5-2
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-4
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 11.

Educational Research Methods 2017

2016-10-24T16:11:16Z

Jobodnar: /* Wrap-up */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-11
**Before class on 4-11, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-13
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-18
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

*4-20 NO CLASS - Spring Carnival

=====Experimental Research Methods (Koedinger)=====
*4-25 Continuation of Causal Inference
*4-27
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*5-2
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-4
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 11.

Educational Research Methods 2017

2016-10-24T16:10:41Z

Jobodnar: /* Experimental Research Methods (Koedinger) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-11
**Before class on 4-11, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-13
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-18
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

*4-20 NO CLASS - Spring Carnival

=====Experimental Research Methods (Koedinger)=====
*4-25 Continuation of Causal Inference
*4-27
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*5-2
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-4
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:09:15Z

Jobodnar: /* Educational Data Mining -- Causal Inference from Data (Scheines) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-11
**Before class on 4-11, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-13
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-18
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

*4-20 NO CLASS - Spring Carnival

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:07:29Z

Jobodnar: /* Flex day (Koedinger) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-6 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:07:12Z

Jobodnar: /* Educational Data Mining -- Learning Curve Analysis (Koedinger) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-28
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-30
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-4
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:06:40Z

Jobodnar: /* Surveys, Questionnaires, Interviews (Kiesler) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-21
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-23
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:05:51Z

Jobodnar: /* NO CLASS – Spring break 3-11 and 3-13 */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-14 and 3-16 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:04:43Z

Jobodnar: /* Psychometrics, reliability, Item Response Theory (Junker) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-28

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*3-2

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-7

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-9 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:03:14Z

Jobodnar: /* Cognitive Task Analysis (CTA) (Koedinger) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-14 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-16 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-21 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-23 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T16:02:03Z

Jobodnar: /* Video and Verbal Protocol Analysis (Lovett, Rosé) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2017 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 24]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 26 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 31 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Feb 2 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 7 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 9 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-11 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-13 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-18 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-20 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T15:59:43Z

Jobodnar: /* Course Intro, Research Questions, Picking Methods (Koedinger) */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-17
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2017]]

*1-19 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2014 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 21]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 23 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 28 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Jan 30 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 4 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 6 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-11 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-13 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-18 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-20 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T14:52:48Z

Jobodnar: /* Class Schedule in Brief */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 17 (T)
* Choosing Qualitative & Quantitative Methods: Jan 19 (R)
* Video and Verbal Protocol Analysis: Jan 24, 26, 31, Feb 2, 7, 9 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 14, 16, 21, 23 (TRTR)
* Educational Design Research: Feb 28 (T)
* Educational Measurement & Psychometrics: Mar 2, 7, 9, (RTR)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 14, 16 (TR)
* Surveys, Questionnaires, Interviews: Mar 21, 23 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 28, 30, Apr 4 (TRT)
* Flex day: Apr 6 (R)
* Educational Data Mining & Causal Inference: Apr 11, 13, 18, (TRT)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 20 (R)
* Experimental Methods: Apr 25, 27, May 2 (TRT)
* Wrap-up: May 4 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-14
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2014]]

*1-16 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2014 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 21]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 23 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 28 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Jan 30 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 4 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 6 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-11 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-13 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-18 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-20 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

Educational Research Methods 2017

2016-10-24T14:31:07Z

Jobodnar: /* Research Methods for the Learning Sciences 05-748 */

===Research Methods for the Learning Sciences 05-748===
Spring 2017 Syllabus Carnegie Mellon University

====Class times====
4:30 to 5:50 Tuesday & Thursday

====Location====
4301 Gates/Hillman

====Instructor====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

====Class URLs====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014
learnlab.org/research/wiki/index.php/Educational_Research_Methods_2014

For reading reports: [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]

===Goals===
The goals of this course are to learn data collection, design, and analysis methodologies that are particularly useful for scientific research in education. The course will be organized in modules addressing particular topics including cognitive task analysis, qualitative methods, protocol and discourse analysis, survey design, psychometrics, educational data mining, and experimental design. We hope students will learn how to apply these methods to their own research programs, how to evaluate the quality of application of these methods, and how to effectively communicate about using these methods.

===Course Prerequisites===
To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.

===Textbook and Readings===
"The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly. You can find it at [http://www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160]

The course registration id is 1620032912010.

Other readings will be assigned in class. See below.

===Flipped Homework: Reading Reports and Pre-Class Assignments===

We are often going to implement "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps students (you!) to "problematize" the topic -- to get a better
sense of what you don't know and what questions you have. It helps instructors focus the class discussion to better avoid belaboring what students already know and to better pursue student needs and interests.

Students will be asked to write "reading reports" before most class sessions. We will use the discussion board on Blackboard ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard]) for this purpose.

Unless otherwise directed by instructors, students should make '''two posts''' on the readings '''before 9am''' on the day of class that those readings are due. If slides for the class are available, please review these as well.

These posts serve multiple purposes: 1) to improve your understanding and learning from the readings, 2) to provide instructors with insight into what aspects of the readings merit further discussion, either because of student need or interest, and 3) as an incentive to do the readings before class!

In general, please come to class prepared to ask questions and give answers.

Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research

*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

You may be asked to do other activities before class, such as answer questions on-line using the [http://assistment.org Assistment system], parts of the an [http://oli.web.cmu.edu/openlearning/ OLI course], or beginning work on an assignment. That way you can come to class with a better appreciation for what you do not understand and need to learn.

===Grading===

There will be assignments associated with each section of the course. Grades will be determined by your performance on these assignments, by before-class preparation activities including reading reports, by your participation in class, and by a final paper.

* Course work
** 30% Before-class preparation, including reading reports, and in-class participation
** 40% Assignments
* Project & final paper - Initial ideas due Feb 15, research question and likely data source due March 30 [satisfied by posting on Blackboard], Final paper due May 10.
** 30% Design a new study based on one or more of these methods that pushes your own research in a new direction.
:# Apply a method from the class to your research. You should not choose a method that you already know well. Because some methods will be introduced after the project proposal date, we are open to a modification in your project to apply the newly introduced method. But, please check with us to get feedback and approval on a proposed change.
:# No more than 15 double-spaced pages. Be efficient. Space is always limited in academic publications and you will find it useful to learn to include only what is important. You can frame your write-up as though the audience were reviewers of a grant proposal or an internal project proposal. As you would in a grant proposal, please include some literature review and discussion of significance of the area you want to investigate. You should also briefly detail plans for participants, explain specifically how you will apply the method, and describe how you will analyze the data.

===Class Schedule in Brief===
* Formulating Good Research Questions: Jan 14 (T)
* Choosing Qualitative & Quantitative Methods: Jan 16 (R)
* Video and Verbal Protocol Analysis: Jan 21, 23, 28, 30, Feb 4,6 (TRTRTR)
** Guest Instructors: Marsha Lovett & Carolyn Rose
* Performing Cognitive Task Analysis: Feb 11, 13, 18, 20 (TRTR)
* Educational Design Research: Feb 25 (R)
* Educational Measurement & Psychometrics: Feb 27, Mar 4, 6 (TRT)
** Guest Instructor: Brian Junker
* NO CLASS – Spring break, Mar 11, 13 (TR)
* Surveys, Questionnaires, Interviews: Mar 18, 20 (TR)
** Guest Instructor: Sara Kiesler
* Educational Data Mining & Learning Curves: March 25, 27, Apr 1 (TRT)
* Flex day: Apr 3 (R)
* Educational Data Mining & Causal Inference: Apr 8, 15, 17 (TTR)
** Guest Instructor: Richard Scheines
* NO CLASS – Spring Carnival, Apr 10 (R)
* Experimental Methods: Apr 22, 24, 29 (TRT)
* Wrap-up: May 1 (R)

===Class Schedule with Readings and Assignments===
'''NOTE:''' This is a "living" document. It carries over elements from the past course offering that may get changed before the scheduled class period.

=====Course Intro, Research Questions, Picking Methods (Koedinger)=====
*1-14
**Read Trochim Chapter 1, particularly sections 1-2d and 1-4. See above for how to get the book -- [[Media:Trochim-Ch01.pdf|but here's Chapter 1.]]
**Do the chpt 1 quiz
**[[Media:CourseIntroGoodQuestions14.ppt|Lecture slides 2014]]

*1-16 Choosing Qualitative & Quantitative Methods
**Read Trochim Chapter 6 on Qualitative Methods. Please order the book, but one last time [[Media:Trochim-Ch06.pdf|here's Chapter 6 if you need it.]]
**Do the chpt 6 quiz
**Read Koedinger, K.R., Booth, J.L., & Klahr, D. (2013). [[Media:InstructionalComplexity2013.pdf‎|Instructional complexity and the science to constrain it]]. ''Science, 342'', 935-937. [[Media:InstructionalComplexity2013.pdf‎|PDF]]
**[Optional reading] Nathan, M., & Alibali, M. (2010). Learning sciences. WIREs Cognitive Science. [[Media:Nathan&Alibali_2010_WIREs_LS.pdf|PDF]]

** Draft Table relating research purposes and methods: [https://docs.google.com/spreadsheet/ccc?key=0AmjMq6vN8egedFlBVmkwV3A4dWNzeHNsNGlqc00yQVE]

=====Video and Verbal Protocol Analysis (Lovett, Rosé) =====

The 2014 plan for these six sessions is in [[Media:PIERResearchMethodsPlan2014.doc|this document]].

By the end of this module, students should be able to:
*Explain what is involved in collecting and analyzing verbal data (including both “hand” and automatic approaches to analysis)
*Recognize when – and explain why – protocol analysis is/is not appropriate to particular research situations.
*Apply protocol analysis methods to already collected and segmented data.

Besides reading and discussing articles, students will complete a coding scheme design assignment.

Four parts of this assignment will be done as homework or in-class work:
*Part A (homework): Between sessions 2 and 3, propose one or more hypotheses and think about how you could use protocol analysis on the given data set to evaluate those hypotheses.
*Part B (homework): By session 5, develop a short coding manual and apply your coding scheme to a subset of the provided data. Bring 2 printouts to class. Also install LightSIDE software on your laptop and make sure it runs (http://ankara.lti.cs.cmu.edu/side/download.html).
*In class Part C: In session 5, swap coding manuals with a classmate and use their coding manual to code the same data they have coded (but not looking at their codes!), and measure reliability.
*Part D (homework): For session 6, prepare data for automatic coding, and bring soft-copy to class along with your laptop.

*Session 1[Jan 21]: Connecting discussion and learning

*In this session we will explore the connection between discussion and learning, specifically investigating how stylistic aspects of language use enable or constrain articulation of ideas at different levels of abstraction, and how they affect how students position themselves or are positioned within an academic discourse. We will explore these issues in connection with different theoretical perspectives on learning including cognitive, sociocognitive, and sociocultural.

*If this is your first exposure to this material, focus mainly on the Howley et al. chapter. If this is your second exposure, skim the Howley et al chapter and focus mainly on the Adamson et al. article and the comparison between the two.

**Howley, I., Mayfield, E. & Rosé, C. P. (2013). Linguistic Analysis Methods for Studying Small Groups, in Cindy Hmelo-Silver, Angela O’Donnell, Carol Chan, & Clark Chin (Eds.) International Handbook of Collaborative Learning, Taylor and Francis, Inc.[[http://www.learnlab.org/research/wiki/images/5/58/Chapter-Methods-Revised-Final.pdf]]

**Adamson, D., Dyke, G., Jang, H. J., Rosé, C. P. (2014). Towards an Agile Approach to Adapting Dynamic Collaboration Support to Student Needs, International Journal of AI in Education 24(1), pp91-121. [[http://www.learnlab.org/research/wiki/images/e/ea/SpecialIssueAdamson-ThirdRevision_Accepted.pdf]]

*Discussion Questions (pick 2 or 3 of these to discuss as they relate to your reading focus):
**What do you see as the advantages and disadvantages of adopting methods from linguistics for the analysis of verbal data from studies of student learning?
**In the Howley chapter, the role of discussion in learning as it is conceptualized within a variety of theoretical frameworks was compared and contrasted. Which do you agree most with and why?
**Pick one of the conversation extracts from the chapter and critique the provided analysis from the perspective of your chosen theoretical framework.
**How could protocol analysis be used to shed light on what was happening in one or more of the the Adamson et al., 2013 studies?
**What do you see as the trade offs between the style of automated process analysis used in the Adamson et al. article and the more linguistically motivated approach discussed in the Howley et al article?

*Session 2[Jan 23 Carolyn]: Overview of Protocol Analysis

**In this discussion, we will begin to explore the basics of collecting verbal protocol data as well as a high-level view of what’s involved in analyzing such data. Whereas the focus in the initial session was on theory, the focus here will be on methodology of protocol analysis by hand. We will explore different uses of verbal data.

**Chi, M. T. H. (1997). Quantifying qualitative analyses of verbal data: A practical guide. The Journal of the Learning Sciences, 63), 271-315.
[[http://chilab.asu.edu/papers/Verbaldata.pdf]]

**Discussion Questions:
***What are the main contrasts between the approach Chi advocates for analysis of verbal data and how she presents verbal protocol analysis?
***What can be gained from using these approaches? Which if either do you have experience with, and if so, can you explain that experience?
***How does Chi present these methodologies as complementary to more formally quantitative methodologies?

**Example Coding Manual [[http://www.learnlab.org/research/wiki/images/9/9c/Negotiation_10.pdf]]

*Session 3[Jan 28 Marsha]: Practical aspects of analyzing verbal data

**In this session we will break down the process of designing a coding scheme into practical steps.

**Gihooly, K. J., Fioratou, E., Anthony, S. H., Wynn, V. (2007). Divergent thinking: Strategies and executive involvement in generating novel uses for familiar objects, British Journal of Psychology, 98, pp 611-625. [[http://www.learnlab.org/research/wiki/images/c/c9/GihoolyEtAl2007.pdf]]

**van Someren, M. W., Barnard, Y. F., & Sandberg, J. A. C. (1994).The Think Aloud Method: A Practical Guide to Modelling Cognitive Processes. New York: Academic Press. Chapter 7 [[http://www.learnlab.org/research/wiki/images/archive/6/63/20130125191704%21VanSch7.pdf]]

*Discussion Questions:
**What, if any, of the steps involved in protocol analysis did you find confusing?
**Which of these steps would you say are most methodologically challenging? most theoretically important?
**How might the steps differ for individual, talk-aloud data vs. collaborative, chat data?

*Session 4[Jan 30 Carolyn]: Methodological considerations related to manual and automatic analysis

**Here we will discuss issues related to reliability and validity, and efficiency of analysis. We will also contrast different types of protocol analyses, namely categorical types of analyses versus word counting approaches.

**Rosé, C. P., Wang, Y.C., Cui, Y., Arguello, J., Stegmann, K., Weinberger, A., Fischer, F., (2008). Analyzing Collaborative Learning Processes Automatically: Exploiting the Advances of Computational Linguistics in Computer-Supported Collaborative Learning, International Journal of Computer Supported Collaborative Learning [[http://www.learnlab.org/research/wiki/images/0/0e/Rose_Analyzing_Collaborative.pdf]]

*Discussion Questions:
**What do you see as the trade-offs between the style of protocol analysis illustrated in this article and that from Adamson et al.?
**What was the most surprising result you read about in the paper? How do the capabilities you read about compare with what you would expect to be able to do with automatic analysis technology?
**What role can you imagine automatic analysis of verbal data playing in your research? Where would it fit within your research process?
**What do you think is the most important caveat related to automatic analysis described in the paper?

*Session 5[Feb 4 Marsha]: Inter-Rater Reliability and When to Use Protocol Data

**In this lecture, we will discuss issues of reliability for protocol data (how to compute Cohen’s kappa and how to resolve coding disagreements). We will also discuss the conditions under which verbal protocol data are/are not appropriate.

**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 1-31). Cambridge, MA: The MIT Press. [Introduction and Summary][[http://www.learnlab.org/research/wiki/images/archive/b/b8/20130125181231%21ProtAna1.pdf]]
**Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis (pp. 78-107). Cambridge, MA: The MIT Press. [Effects of Verbalization] [[http://www.learnlab.org/research/wiki/images/archive/f/fe/20130125181401%21ProtAnalysis2.pdf]]

*Discussion Questions:
**What are the key features that make verbal protocols appropriate/not?
**What can researchers do to collect and analyze such data most effectively?

*Session 6[Feb 6 Carolyn and Marsha]: Tools For Supporting Protocol Analysis

**In this session we will introduce some new technology for facilitating protocol analysis tasks. Students will gain hands on experience with a new technology called SIDE Tools [[http://ankara.lti.cs.cmu.edu/side/download.html]]. You will work with the data you coded in the last session. Please read the user’s manual.

*Discussion Questions:
**What evidence do you as a human use to distinguish between the codes in your coding scheme? How much of this evidence do you think a computer would be able to take advantage of?
**Looking at your coded data, which aspects do you predict will be easy to automatically code, and which do you think will be too hard?

=====Cognitive Task Analysis (CTA) (Koedinger) =====
*2-11 Empirical Cognitive Task Analysis (CTA) via Structured Interviews of Experts
**[[Media:Clark CTA In Healthcare Chapter 2012.pdf |Clark et al (2012) on Cognitive Task Analysis and improving instruction]]
**Do two posts on Blackboard.
***One point of reflection for you on the Clark et al reading is to compare and contrast with recommendations for collection and analysis from van Someren et al and from Ericsson et al. (If you saw Bror Saxberg's PIER talk last year, you may have heard that Kaplan is using CTA, with Clark's advice, to revise and improve their courses.)
**Optional readings:
***[[Media:Lovett01CandI.pdf|Kinds of CTA and instructional design by Marsha Lovett]]
***[[Media:Feldon_Timmerman_etal_2010.pdf|CTA for improving instruction of Biology research by David Feldon]]

*2-13 Rational CTA via Cognitive Modeling
**Zhu X., Lee Y., Simon H.A., & Zhu, D. (1996). Cue recognition and cue elaboration in learning from examples. In Proceedings of the National Academy of Sciences 93, (pp. 1346±1351). [[Media:PNAS-1996-Zhu-Simon.pdf|PNAS-1996-Zhu-Simon.pdf]]
**[Optional reading] Chapter 2: How Experts Differ From Novices in Bransford, J. D., Brown, A., & Cocking, R. (2000). (Eds.), How people learn: Mind, brain, experience and school (expanded edition). Washington, DC: National Academy Press. [[Media:HowPeopleLearnCh2.pdf|HowPeopleLearnCh2.pdf]]
***Besides being an interesting read, a key point of this reading is the nature of expert knowledge (declarative and procedural) and how it is highly "conditionalized". Their discussion of adaptive expertise is also important and interesting.
**[Optional reading] Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166. [[Media:Zhu&Simon-1987.pdf|Zhu&Simon-1987.pdf]]

*2-18 Doing CTA for higher-level thinking/learning skills
**Azevedo et al on think alouds during learning from hypermedia [[Media:AzevedoMoosJohnson&Chauncey2010.pdf|AzevedoMoosJohnson&Chauncey2010.pdf]]
**Aleven, V., McLaren, B., Roll, I., & Koedinger, K. R. (2004). Toward tutoring help seeking: Applying cognitive modeling to meta-cognitive skills. In J.C. Lester, R.M. Vicari, & F. Parguacu (Eds.) Proceedings of the 7th International Conference on Intelligent Tutoring Systems, 227-239. Berlin: Springer-Verlag. [[Media:AlevenITS2004.pdf|AlevenITS2004.pdf]]
**Klahr, D., & Carver, S.M. (1988). Cognitive objectives in a LOGO debugging curriculum: Instruction, learning, and transfer. Cognitive Psychology, 20, 362-404. [[Media:Klahr&carver88.pdf|Klahr&carver88.pdf]]
***Pick '''one''' of these readings to focus on and skim the other two. Target your first post on that reading (and make clear which one it was). Your second post can be on any of the three. These readings illustrate the use of Cognitive Task Analysis (CTA) for higher level thinking and learning skills. The Klahr & Carver reading shows how CTA can facilitate the design of instruction that achieves a substantial level of transfer. The Azevedo et al and Aleven et al readings provide examples of CTA at the level of metacognitive skills or learning skills. When you skim all three, pay particular attention to 1) what are tasks the authors are analyzing, 2) what is their goal, 3) what is(are) the method(s) of analysis, and 4) what modeling approaches do the authors use to represent the output of their analysis: Do they use any of production rules, goal trees, semantic nets, hierarchical task models, or other?
*Other possible readings:
**Kinds of CTA and instructional design: Lovett [[Media:Lovett01CandI.pdf|Lovett01CandI.pdf]]
**Relevant to cognitive modeling: Newell & Simon [[Media:Human_Problem_Solving.pdf|Human_Problem_Solving.pdf]]
**A form of CTA with young kids: Siegler, R.S. (1976). Three aspects of cognitive development. Cognitive Psychology, 8 (4), 481-520, Elsevier. [[Media:Siegler76.pdf|Siegler76.pdf]]

*2-20 Empirical quantitative CTA via Difficulty Factors Assessment
**Read: Koedinger, K.R. & Nathan, M.J. (2004). The real story behind story problems: Effects of representations on quantitative reasoning. ''The Journal of the Learning Sciences, 13'' (2), 129-164. [[Media:Koedinger-Nathan-LS04.pdf|Koedinger-Nathan-LS04.pdf]]
***In addition to think aloud, another empirical approach to Cognitive Task Analysis is to compare student performance on a space of similar tasks designed to test specific hypotheses about the knowledge demands of those tasks. We have called this approach "Difficulty Factors Assessment" and the Koedinger & Nathan paper is an early example. The former assignment below, which is focused on rational CTA, provides an example of the similarity in the logic of contrast used in Difficulty Factors Assessment and the contrast between the two tasks or solutions one can do in a rational CTA. Skim Koedinger & MacLaren to see another example of a production rule model and of a method of quantitative evaluation of that model by fitting it to coding categories from a solution protocol analysis.
**Skim: Koedinger, K.R., & MacLaren, B. A. (2002). Developing a pedagogical domain theory of early algebra problem solving. CMU-HCII Tech Report 02-100. Accessible via http://reports-archive.adm.cs.cmu.edu/hcii.html [[Media:KoedingerMacLaren02.pdf|KoedingerMacLaren02.pdf]]
**Do two posts on these readings.

**Other optional readings
***[[Media:Applying-CTA-assignment.docx|See prior CTA assignment.]]
***Koedinger, K.R. & McLaughlin, E.A. (2010). Seeing language learning inside the math: Cognitive analysis yields transfer. In S. Ohlsson & R. Catrambone (Eds.), ''Proceedings of the 32nd Annual Conference of the Cognitive Science Society.'' (pp. 471-476.) Austin, TX: Cognitive Science Society. [[Media:Koedinger-mclaughlin-cs2010.pdf|Koedinger-mclaughlin-cs2010.pdf]]
***Rittle-Johnson, B. & Koedinger, K. R. (2001). Using cognitive models to guide instructional design: The case of fraction division: In Proceedings of the Twenty-Third Annual Conference of the Cognitive Science Society, (pp. 857-862). Mahwah,NJ: Erlbaum. [[Media:Rittle-Johnson-Koedinger-cogsci01.pdf|Rittle-Johnson-Koedinger-cogsci01.pdf]]
***Koedinger, K. R., Corbett, A. C., & Perfetti, C. (2012). The Knowledge-Learning-Instruction (KLI) framework: Bridging the science-practice chasm to enhance robust student learning. ''Cognitive Science''. [[Media:KLI-paper-v5.13.pdf|KLI-paper-v5.13.pdf]]

=====Psychometrics, reliability, Item Response Theory (Junker)=====

*NEW ASSIGNMENTS [Plans for these classes were communicated by Brian Junker via email.]

*2-25

**Quick introduction to the R statistical language

**Please complete and bring comments & questions to class on Tues Feb 28.
**Please download research_methods_r_assignment.zip from http://www.stat.cmu.edu/~brian/PIER-methods/. The Zip file contains three further files:
*** R-preassignment.pdf - instructions for this assignment
*** r-tutorial-1.R - examples of statistical things that you will do in R, for this assignment
*** thermo11_data_integrated.csv - a data set for the examples.

*2-27

1. From Trochim:

A. Chapter 3 - the vocabulary of measurement

B. Chapter 5 - on constructing scales (it's ok to focus
on the material up through sect 5.2a; the rest is
more of a skim [but I'd be happy to talk about that
in class also])

2. On item response theory (IRT), a set of statistical models that are used
to construct scales and to derive scores from them, especially in education
and psychological research:

A. [[Media:Harris-article.pdf|Harris Article (PDF)]]

Please take and self-score the test at the end of
this article. Count each part of question one as
one point, and each of the remaining three questions
as one point (no partial credit!). Bring your 8
scores to class. E.g. if you missed 1(c) and (d), and
you also missed question 4, then you would bring to
class the following scores:

1 1 0 0 1 1 1 0

If you missed 1(a) and (b) and question 2, bring the
following scores:

0 0 1 1 1 0 1 1

(note that the total score is 5 in both cases, but
the pattern of rights and wrongs differs; it is the
pattern that we are interested in).

B. Please browse *online* through pp 1-23 of the pdf at
[http://www.metheval.uni-jena.de/irt/VisualIRT.pdf].

The math is a bit heavy going but there are links
to apps that illustrate various points in the
harris article.

So skim the math and play with the apps.

*3-4

The assignment for this lecture has two parts.

** (A) An R assignment TBA. This you can actually email to my by Fri Mar 7.
** (B) The readings below.

On Tue we will discuss whatever of A and/or B seem interesting

1. "Psychometric Principles in Student Assessment" by Mislevy et al ([[Media:mislevy-principles-2001.pdf|Mislevy (PDF)]])

Read through p 18. This is a more modern modern look at some of
the same issues that are addressed in Trochim's chapters.

The remainder of this paper surveys various probabilistic models
for the "measurement model" portion of Mislevy's framework (Figure
1). It is quite interesting but we will not pursue it.

2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma ([[Media:junker-sijtsma-apm-2001.pdf|Junker, Sijtsma (PDF)]])

Please read up through p 266 only.

The math is a bit heavy going so please try to read around it to
see what the point of the article is.

We will try to look at some of the data in the article as examples
in lecture 2.

*3-6 Continued discussion of Psychometrics [moved Design Research as option for Flex Day]

=====NO CLASS – Spring break 3-11 and 3-13 =====

=====Surveys, Questionnaires, Interviews (Kiesler) =====
* [Plans for these classes were communicated by Kiesler (& Koedinger) via email.]
*3-18
**Reading: Trochim Ch 4 and 5
***You already read Ch 5 for the Psychometric section, so just review it. For both chapters, answer Trochim's on-line questions before and/or after reading (answering the questions before gives you goals for reading). For discussion board posts, do one post on how have or might use a survey (e.g., of student attitudes) in your own research. Make another post about Chapter 4, such as something you learned, a question you have, or an answer to someone else's question.
*3-20
**Do the following homework assignment [[Media:Arm-modQuestEduc.doc]]. Sara directs: Keep the text that's there and fill in answers, working through it step by step. I'm just as interested in your revisions as in the final version. Est time 45 minutes.
**Readings
***Tourangeau, Roger, and T. Yan. 2007. "Sensitive questions in surveys." Psychological Bulletin, 133(5): 859-883. [[Media:Tourangeau_SensitiveQuestions.pdf]]
***Tourangeau, R. (2000). “Remembering what happened: Memory errors and survey reports.@ In A. Stone, J. Turkkan, C. Bachrach, J. Jobe, H. Kurtzman, & V. Cain (Eds.), The Science of Self-Report: Implications for research and practice (pp. 29-48). Englewood Cliffs, N.J.: Lawrence Erlbaum. [[Media:Tourangeau_RememberingWhatHappened.pdf]]

=====Educational Data Mining -- Learning Curve Analysis (Koedinger) =====
*3-25
**BRING YOUR LAPTOP FOR ALL THESE SESSIONS
**Two in-class activities: 1) Make progress toward your course project (e.g., further write-up of your research question, justify method selection, search for relevant data) and 2) Work on learning curve assignment (due on Thursday by 9am).
***Start on the assignment BEFORE CLASS and complete up to step B4, requesting access to the data.
**Read the following paper and make two posts as usual.
***Stamper, J. & Koedinger, K.R. (2011). Human-machine student model discovery and improvement using data. In J. Kay, S. Bull & G. Biswas (Eds.), Proceedings of the 15th International Conference on Artificial Intelligence in Education, pp. 353-360. Berlin: Springer.[[Media:Stamper-Koedinger-AIED2011.pdf| Stamper-Koedinger-AIED2011.pdf]]
***'''Optional:'''Ritter, F.E., & Schooler, L. J. (2001). The learning curve. In W. Kintch, N. Smelser, P. Baltes, (Eds.), International Encyclopedia of the Social and Behavioral Sciences. Oxford, UK: Pergamon. [[Media:RittterSchooler01.pdf | RittterSchooler01.pdf]]
**'''Assignment:''' The assignment ([[Media:Learning-curve-assignment-2014.doc | Learning-curve-assignment-2014.doc]]) is a tutorial on using DataShop to begin analyzing learning curves. Upload to Blackboard (or email to me) by 9am on Thursday.
*3-27
**Read the following paper and make two posts as usual.
***Koedinger, K.R., McLaughlin, E.A., & Stamper, J.C. (2012). Automated student model improvement. In Yacef, K., Zaïane, O., Hershkovitz, H., Yudelson, M., & Stamper, J. (Eds.), Proceedings of the 5th International Conference on Educational Data Mining, pp. 17-24. [[Media:KoedingerMcLaughlinStamperEDM12.pdf|KoedingerMcLaughlinStamperEDM12.pdf]]
**In-class activity: Start on one of the two exercises (A or B) below. Provide a brief writeup in response to each of the numbered steps and include a summary of the result you achieved (e.g., did you get a more predictive model as measured by AIC, BIC, or cross validation). Turn in this writeup and the supporting file (KC model table or R file) on Blackboard. Make significant progress before class next Tuesday (get to a point where you are stuck or can see your way to the end). Due by end of day on Wednesday, 4-2.
*4-1
**In-class: Bring your laptop to work on (finish!) your chosen exercise (A or B).
**Read the following paper and make two posts as usual.
***Zhang, X., Mostow, J., & Beck, J. E. (2007, July 9). All in the (word) family: Using learning decomposition to estimate transfer between skills in a Reading Tutor that listens. AIED2007 Educational Data Mining Workshop, Marina del Rey, CA [[Media:AIED2007_EDM_Zhang_ld_transfer.pdf|AIED2007_EDM_Zhang_ld_transfer.pdf]]
***'''Optional:''' Roberts, Seth, & Pashler, Harold. (2000). How persuasive is a good fit? A comment on theory testing. Psychological Review, 107(2), 358 - 367. [[Media:2000_roberts_pashler.pdf]]
***'''Optional:''' Schunn, C. D., & Wallach, D. (2005). Evaluating goodness-of-fit in comparison of models to data. In W. Tack (Ed.), Psychologie der Kognition: Reden and Vorträge anlässlich der Emeritierung von Werner Tack (pp. 115-154). Saarbrueken, Germany: University of Saarland Press. [[Media:GOF.doc]]

Do A or B:
A. Modify a KC model in a DataShop dataset
1. What is the DataShop dataset you modified?
2. Describe how you used the HMST procedure (from Stamper paper)
to identify a KC to try to improve
3. Show how you recoded that KC with new KCs (turn in your modified
KC file) & describe why you made the change you did
4. After importing your new KC model to DataShop, did it improve the
predictions (are any of the metrics, AIC, BIC, or cross validation)?
(Caution: Make sure your new KC model labels the same number of
observations as the KC model you are modifying.)

B. Use R to create an alternative statistical model to AFM
1. Approximate afm in R using either glm or lmer. How do the parameter
estimates and metrics (AIC and BIC) compare with results in DataShop?
2. Modify the regression equation to try to improve the prediction.
Some options include: a) adding a student by KC interaction (there
are just main effects of student and KC in AFM), b) adding student
slopes (there is just a KC slope in AFM), c) counting success and
failure opportunities separately (both kinds of opportunities are
lumped together in AFM), d) using log of Opportunity, e) including
step (perhaps as a random effect) ...
3. Turn in your R file including metrics (log-liklihood, parameters,
AIC, BIC) on the statistical models you compared
4. Summarize whether or not your modification changes model fit (log
liklihood), changes the number of parameters (from what to what),
and, most importantly, improves prediction (as measured by AIC or BIC)

===== Flex day (Koedinger) =====
*4-3 To be used in case of rescheduling, for a student-driven topic, and/or for Review of Projects or Past Topics
** We will wrap up on EDM for learning curves (option1) and, time permitting, give work time for your project.
***Option1. More on Educational Data Mining
***Option2. Return to Design Research & Qualitative Methods (Koedinger)
***Trochim Ch 8 (stop before 8.5), Ch 13 (stop before 13.3)
***Barab, S., & Squire, K. (2004). Design-based research: Putting a stake in the ground. The Journal of the Learning Sciences, 13(1). [[Media:2004 Barab Squire.pdf|PDF]]
***Optional reading: Chapter on Design Research in Handbook of Learning Sciences

=====Educational Data Mining -- Causal Inference from Data (Scheines) =====
*4-8
**Before class on 4-8, do Unit 2 in the OLI course Empirical Research Methods
Go to: http://oli.cmu.edu/learn-with-oli/see-our-free-open-courses/
Scroll down and click on the rightmost tab, "Prior work (5)"
Click on "Empirical Research Methods" and then on "[Enter Course]"
Click on "CMU users sign in here" to login with your CMU account
or "Enter Without an Account"
Complete "UNIT 2: Regression, Prediction and Causation"
**See this website for relevant material: http://www.hss.cmu.edu/philosophy/casestudiesworkshop.php (It is for a workshop on "Case Studies of Causal Discovery with Model Search")
**Scroll down to the schedule. Videos and slides are posted for most of the talks. Three that are relevant to this class are:
***a) Tutorial on causal learning (my tutorial on Tetrad)
***b) Educational Research I (overview of causal discovery in educational research)
***c) Educational Research II (Martina explaining the paper you are assigned)
**There are also case studies from economics, fMRI, genetics, biology, as well as educational research.

*4-10 NO CLASS - Spring Carnival

*4-15
**Read and post about Scheines, R., Leinhardt, G., Smith, J., and Cho, K. (2005). Replacing lecture with web-based course materials. Journal of Educational Computing Research, 32, 1, 1-26. [[Media:Scheines jecr revised.doc | PDF]]

*4-17
**Read and post about [[Media:RauScheinesAlevenRummel_EDM2013_camera-ready_final.pdf | Rau, Scheines, Aleven, & Rummel (2013]]

=====Experimental Research Methods (Koedinger)=====
*4-22 Continuation of Causal Inference
*4-24
**Reading: Trochim Ch 7 and 9
**Do two posts on Blackboard.
**OLD Slides: [[Media:L02_--_Basic_Research_and_Experimental_Methods.ppt|Experimental_Methods.ppt]] and [[Media:L03-True-Experiments.ppt|True-Experiments.ppt]]
*4-29
**Reading: Trochim Ch 10
**OLD Slides: [[Media:L04-quasi-experiments.ppt|Quasi-Experiments.ppt]]
*5-1
**Reading: Trochim Ch 14
**Optional: Try ANOVA module of OLI Statistics course

=====Wrap-up=====
If needed, schedule a course wrap-up

Final project is due May 9.

E-Learning Design Principles and Methods 2016

2015-08-25T12:54:44Z

Jobodnar: /* Learning By Doing Principles 10-27 to 11-24 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 9-1- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to improve model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Project Presentations
**Dec 10 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-10
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-17
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Determining instructinal goals (task)
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle & 5.Contiguity Principles
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
**Readings: 5.Contiguity Principle (24 pages)
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Modality Principles & Redundancy Principle
** Reading: 6.Modality Principle (18 pages)
**Do quizzes for Chapter 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Personalization Principle
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-20 Midterm Review
****Do quizzes for Chapter 8

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-12
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 12-1

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17

*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-8 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-25T12:51:48Z

Jobodnar: /* Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 9-1- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to improve model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Project Presentations
**Dec 10 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-10
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-17
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Determining instructinal goals (task)
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle & 5.Contiguity Principles
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
**Readings: 5.Contiguity Principle (24 pages)
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Modality Principles & Redundancy Principle
** Reading: 6.Modality Principle (18 pages)
**Do quizzes for Chapter 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Personalization Principle
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-20 Midterm Review
****Do quizzes for Chapter 8

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-8 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-25T12:48:47Z

Jobodnar: /* Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 9-1- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to improve model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Project Presentations
**Dec 10 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-10
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-17
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Determining instructinal goals (task)
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle & 5.Contiguity Principles
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Modality Principles & Redundancy Principle
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Personalization Principle
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-20 Midterm Review
****Do quizzes for Chapter 8

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-8 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-25T12:40:58Z

Jobodnar: /* Instructional Goals and Cognitive Task Analysis 9-15 to 9-22 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 9-1- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to improve model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Project Presentations
**Dec 10 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-10
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-17
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Determining instructinal goals (task)
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-8 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-25T12:37:39Z

Jobodnar: /* E-Learning Introduction 9-1 to 9-10 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 9-1- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to improve model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Project Presentations
**Dec 10 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-10
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-17
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-8 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:32:53Z

Jobodnar: /* Class Schedule in Brief */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control? '''(need substitute for this class)'''
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-7 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:31:41Z

Jobodnar: /* Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- Bring laptop to exam */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-7 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 17, 1-4pm in GHC5222 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:31:09Z

Jobodnar: /* Final Project Due on 12-12 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-7 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-18=====
*12-18 Project Due

=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:30:51Z

Jobodnar: /* Project Presentations 11-20 to 12-4 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 12-1 to 12-10=====

*12-1 Project Presentations
*12-3 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-18. It should include the reflection statement (see the project assignment handout).
*12-7 Project Presentations
*12-10 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:29:22Z

Jobodnar: /* Learning By Doing Principles 10-21 to 11-18 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-27 to 11-24 =====
*10-27 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-29 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*11-3 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*11-5 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-10 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-12 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-17 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-19 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-24 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17
*11-26 Thanksgiving, no class

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:27:14Z

Jobodnar: /* Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22=====
*9-24 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*10-1
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*10-6 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-8 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-13 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-15 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-20 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-22 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-11 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-13 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:25:57Z

Jobodnar: /* Instructional Goals and Cognitive Task Analysis 9-9 to 9-16 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-15 to 9-22=====

*9-15 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-17 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-22 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-23 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*9-25
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*9-30 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-2 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-9 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-14 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-16 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-11 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-13 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:24:37Z

Jobodnar: /* E-Learning Introduction 8-26 to 9-4 */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 9-1 to 9-10''=====

*9-1 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*9-3 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-8 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-10 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-23 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*9-25
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*9-30 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-2 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-9 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-14 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-16 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-11 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-13 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

E-Learning Design Principles and Methods 2016

2015-08-10T13:23:08Z

Jobodnar: /* Class Schedule in Brief */

====Course Details====
Course number: 05-823

Semester: Fall 2015

Carnegie Mellon University

=====Class times=====
9:00 to 10:20 Tuesday & Thursday

=====Location=====
Gates Hillman Center (GHC) Room 5222

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instructor.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in your own educational technology design project.

====Flipped Homework: Reading Quizzes and Reading Reports ====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am'' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Laptop Policy====

Given that class discussion is a major part of the course, laptops, cell phones, and smart phones are not to be used in class during Lecture days. Failure to listen to this will result in a reduction in your participation grade. During testing days (marked as such on the schedule), however, you will need your laptop.

Students have the option of using a laptop during presentations '''only if''' they are doing so to take notes and submit those notes to the full class for example on blackboard. To facilitate note taking during Lecture days, lecture slide handouts may be provided, if requested.

If interested in what educational research says about laptop use in class, or multi-tasking more generally, you might look at (available on the course BlackBoard):

*Fried, C. B. (2008). In-class laptop use and its effects on student learning. Computers & Education, 50, 906–914.

*Kirschner, P. A., & Merriënboer, J. J. V. (2013). Do learners really know best? Urban legends in education. Educational Psychologist, 48(3), 169–183. doi:10.1080/00461520.2013.80439

*Kraushaar, J. M., & Novak, D. C. (2010). Examining the affects [sic] of student multitasking with laptops during the lecture. Journal of Information Systems Education, 21(2), 241-251.

*Wood, E., Zivcakova, L., Gentile, P., Archer, K., De Pasquale, D., & Nosko, A. (2012). Examining the impact of off-task multi-tasking with technology on real-time classroom learning. Computers & Education, 58(1), 365-374. doi:10.1016/j.compedu.2011.08.02

====Grading ====

* 40% Final Project [[Media:E-learning-project-assignment-2014-d.docx|Project assignment]]
**Six parts of final project
**Final project submission
* 5% E-Learning examples assignment
* 15% Midterm exam
* 15% Pre-class quizzes & reading reports
* 20% Final Exam
* 5% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 91- to 9-10
**Sept 1 Overview; Examples Assignment; Project
**Sept 3 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 8 2.How People Learn; KLI KC's
**Sept 10 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-15 to 9-22
**Sept 15 Determining instructional goals (tasks)
**Sept 17 Cognitive Task Analysis and Think Alouds
**Sept 22 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-24 to 10-22
**Sept 24 4.Multi-media Principle & 5.Contiguity Principle
**Sept 29 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Oct 1 (con't)
**Oct 6 6.Modality Principle & 7.Redundancy Principle
**Oct 8 CTA to model building & instructional design
**Oct 13 CTA via Data Mining
**Oct 15 8.Coherence Principle & 9.Personalization Principle
**Oct 20 Midterm review &
**Oct 22 Midterm exam & Flex topic '''Bring laptop to class'''
*Learning By Doing Principles 10-27 to 11-24
**Oct 27 10.Segmenting and Pretraining
**Oct 29 KLI & Selecting appropriate instructional principles
**Nov 3 11.Leveraging Examples in E-Learning
**Nov 5 12.Does Practice Make Perfect
**Nov 10
**Nov 12 14.Who’s in Control?
**Nov 17 15.E-Learning to Build Problem Solving Skill
**Nov 19 16.Simulations and Games
**Nov 24 17.Applying the Guidelines
**Nov 26 Thanksgiving, no class
*Project Presentations 12-1 to 12-10
**Dec 1 Project Presentations
**Dec 3 Project Presentations
**Dec 8 Thanksgiving, no class
**Dec 10 Project Presentations
**Dec 15 Project Presentations
*Final Project due Dec 18
*Final Exam Date - Thurs Dec 17, 1-4pm in GHC 5222 '''Bring laptop to class'''

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 8-26 to 9-4''=====

*8-26 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment-mm.docx|Examples (click to get)]] is due next Thursday, 9-4
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2014-c.docx|Project]] step 1 is due in 16 days on Thursday, 9-11
**NOTE: See reading assignment for next time on next date.

*8-28 E-learning intro and KLI Framework events
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings & b) associated quizzes on Blackboard

*9-2 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
**Do the quizzes for Chpt2 and KLI#1 on Blackboard.
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-4 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
**Reading: KLI sect 4-5 (12 pages)
**Do the quizzes for Chpt3 and KLI#2 on Blackboard.
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment-mm.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do the quizzes on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2a is due on 9-25
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do the quizzes on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
***Posts: Do the quizzes on the reading.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
**Optional Reading: Working Minds: A practitioner's Guide to Cognitive Task Analysis, [[Media:WorkingMinds_Chap_1.pdf|Ch 1]] and [[Media:WorkingMinds_Ch_2.pdf|Ch 2]]
*** Do the quizzes for the reading.

*9-23 Empirical Cognitive Task Analysis: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do the quizzes on the reading.
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2a.

*9-25
**Due: P2a:Benchmark Tasks & Rational Cognitive Task Analysis
**Finish discussion for DFA and Heffernan from last time
**Introduce step P2b

*9-30 Contiguity & Modality Principles
**Readings: 5.Contiguity Principle (24 pages) & 6.Modality Principle (18 pages)
**Do quizzes for Chapters 5 and 6
**Class activity: Work on P2b. How will you collect data?

*10-2 Cognitive Task Analysis to model building & instructional design
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf | Clark_CTA_In_Healthcare_Chapter_2012]]
**Optional readings:[[Media:Working Minds-Ch5-interviews.pdf |Working Minds Ch 5]];[[Media:Clarketal2007-CTAchapter proof-1.pdf | Clark et al., 2007 Chapter proof]]
**Do two posts for reading on Discussion Board
**Class activity: Work on P2b. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)
**Do quizzes for Chapter 7 and e-learning data to improvement paper

*10-9 Coherence Principle & Midterm Review
**Reading: 8.Coherence Principle (28 pages)
**Do quizzes for Chapter 8

*10-14 Personalization Principle
**Reading: 9.Personalization Principle (26 pages)
**Due: P2b: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals
**Optional Practice quiz for Chapter 9 (not included in total quiz points)

*10-16 Midterm exam '''Bring laptop to class'''
**Optional Readings:
***[http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
***[[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quizzes for Chapter 10

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**Assignment: P3a and 3b are due 11-6
**Do quizzes for reading.

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)
**Do quizzes for Chapter 11

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)
**Do quizzes for Chapter 12

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)
**Do quizzes for Chapter 13

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? (30 pages)
**Do quizzes for Chapter 14

*11-11 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)
**Do quizzes for Chapter 15
**DUE: P3a: Assessment & Initial Instructional Design and P3b: Instructional Design Prototyping & Testing
**Assignment: P4 is due 11-25

*11-13 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**Do quizzes for Chapter 16

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)
**Do quizzes for Chapter 17

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
** DUE: P4: Research Design
**Assignment: Final Project is due 12-12. It should include the reflection statement (see the project assignment handout).
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due
=====Final Exam Date - Thurs December 11, 1-4pm in GHC4211 -- '''Bring laptop to exam'''=====

File:WorkingMinds Ch. 1 & 2.pdf

2014-09-12T18:10:20Z

Jobodnar:

E-Learning Design Principles and Methods 2016

2014-08-20T18:39:33Z

Jobodnar: /* IN PROGRESS!!!! */

==IN PROGRESS!!!!==
====Course Details====
Course number: 05-823

Semester: Fall 2014

Carnegie Mellon University

=====Class times=====
10:30 to 11:50 Tuesday & Thursday

=====Location=====
104 South Craig St (SCR)

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instruction.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in educational technology development.

====Flipped Homework: Reading Reports and Reading Quizzes====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am''' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Grading====

* 55% Final Project
** 45% Six parts of final project, 7.5% each
** 10% Final project submission
* 5% E-Learning examples assignment
* 10% Peer review and feedback
* 10% Pre-class quizzes & reading reports
* 10% Chapter summary
* 10% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 8-26 to 9-4
**Aug 26 Overview; Examples Assignment; Project
**Aug 28 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 2 2.How People Learn; KLI KC's
**Sept 4 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-9 to 9-16
**Sept 9 Determining instructional goals (tasks)
**Sept 11 Guest lecture
**Sept 16 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16
**Sept 18 4.Multi-media Principle
**Sept 23 Empirical Cognitive Task Analysis: Think aloud
**Sept 25 5.Contiguity Principle
**Sept 30 CTA: DFA & Model building
**Oct 2 6.Modality Principle & 7.Redundancy Principle
**Oct 7 CTA & Designing Assessments for Continual Improvement
**Oct 9 Midterm review; Flex topic
**Oct 14 8.Coherence Principle
**Oct 16 9.Personalization Principle
*Learning By Doing Principles 10-21 to 11-18
**Oct 21 10.Segmenting and Pretraining
**Oct 23 KLI & Selecting appropriate instructional principles
**Oct 28 11.Leveraging Examples in E-Learning
**Oct 30 12.Does Practice Make Perfect
**Nov 4 13.Learning Together Virtually
**Nov 6 14.Who’s in Control?
**Nov 11 15.E-Learning to Build Problem Solving Skill
**Nov 13 16.Simulations and Games
**Nov 18 17.Applying the Guidelines
*Project Presentations 11-20 to 12-5
**Nov 20 Project Presentations
**Nov 25 Project Presentations
**Nov 27 Thanksgiving, no class
**Dec 2 Project Presentations
**Dec 4 Project Presentations
*Final Project due Dec 12

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 8-26 to 9-4''=====

*8-26 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment.docx|Examples (click to get)]] is due next Thursday, 9-5
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2013.docx|Project]] step 1 is due in 16 days on Thursday, 9-12
**NOTE: See reading assignment for next time on next date.

*8-28 E-learning intro and KLI Framework events [[Media:L01-e-learning-promises-pitfalls.pptx|(Click here for slides)]]
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings, b) associated quiz & c) discussion board post on Blackboard

*9-2 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
***Pre-class quiz: Answer questions for Chpt2 Quiz on Blackboard (5 minutes)
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-4 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
***Pre-class quiz: Answer questions for Chpt3 Quiz on Blackboard
**Reading: KLI sect 4-5 (12 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do two posts on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds by guest lecturer Vincent Aleven
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2 is due on 9-26
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do two posts (total) on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
***Posts: Do two posts (total) on the readings.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
*** Do the quiz and one post.

*9-23 Empirical CTA: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do two posts on the reading
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2.

*9-25 Contiguity Principle
**Reading: 5.Contiguity Principle (24 pages)
**Due: P2:Benchmark Tasks & Rational Cognitive Task Analysis
**Class activity: Peer review of P2

*9-30 From CTA to model building & instructional design
**Reading: [[Media:CS02-koedinger-terao-rev.pdf | Picture Algebra paper]]
**Class activity: Work on P3. How will you collect data?

*10-2 Modality Principle
**Reading: 6.Modality Principle (18 pages)
**Class activity: Work on P3. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)

*10-9 Flex topic: Design & Urban Legends Do Learners Really Know Best? Urban Legends in Education
**Reading: [http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
**Optional reading: [[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
**Due: P3: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals

*10-14 Richard Clark visit to class [Was previously Coherence Principle]
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf‎ | Clark_CTA_In_Healthcare_Chapter_2012.pdf‎]]
**See his talk on Monday at 3pm in 6115 Gates
**Do 2 posts and come prepared to ask him good questions

*10-16 Coherence and Personalization Principles
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quiz and one post

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**DUE: P4: Assessment & Initial Instructional Design
**Assignment: P5 is due 11-7

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? 30 pages)

*11-11 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-26

*11-13 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)

=====Project Presentations 11-20 to 12-4=====

*11-20 Project Presentations
*11-25 Project Presentations
**Faculty course evaluation
**Changed "Due: P6: Research Design" to revise your project with a particular focus on improving steps 3 and 5. Turn all your revisions in as part of the final project and include the reflection statement (see the project assignment handout).
**Assignment: Final Project is due 12-12
*11-27 Thanksgiving, no class
*12-2 Project Presentations
*12-4 Project Presentations

=====Final Project Due on 12-12=====
*12-12 Project Due

E-Learning Design Principles and Methods 2016

2014-08-20T18:37:22Z

Jobodnar: /* Project Presentations 11-20 to 12-4 */

E-Learning Design Principles and Methods 2016

2014-08-20T18:35:10Z

Jobodnar: /* Learning By Doing Principles 10-21 to 11-18 */

==IN PROGRESS!!!!==
====Course Details====
Course number: 05-823

Semester: Fall 2014

Carnegie Mellon University

=====Class times=====
10:30 to 11:50 Tuesday & Thursday

=====Location=====
104 South Craig St (SCR)

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instruction.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in educational technology development.

====Flipped Homework: Reading Reports and Reading Quizzes====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am''' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Grading====

* 55% Final Project
** 45% Six parts of final project, 7.5% each
** 10% Final project submission
* 5% E-Learning examples assignment
* 10% Peer review and feedback
* 10% Pre-class quizzes & reading reports
* 10% Chapter summary
* 10% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 8-26 to 9-4
**Aug 26 Overview; Examples Assignment; Project
**Aug 28 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 2 2.How People Learn; KLI KC's
**Sept 4 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-9 to 9-16
**Sept 9 Determining instructional goals (tasks)
**Sept 11 Guest lecture
**Sept 16 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16
**Sept 18 4.Multi-media Principle
**Sept 23 Empirical Cognitive Task Analysis: Think aloud
**Sept 25 5.Contiguity Principle
**Sept 30 CTA: DFA & Model building
**Oct 2 6.Modality Principle & 7.Redundancy Principle
**Oct 7 CTA & Designing Assessments for Continual Improvement
**Oct 9 Midterm review; Flex topic
**Oct 14 8.Coherence Principle
**Oct 16 9.Personalization Principle
*Learning By Doing Principles 10-21 to 11-18
**Oct 21 10.Segmenting and Pretraining
**Oct 23 KLI & Selecting appropriate instructional principles
**Oct 28 11.Leveraging Examples in E-Learning
**Oct 30 12.Does Practice Make Perfect
**Nov 4 13.Learning Together Virtually
**Nov 6 14.Who’s in Control?
**Nov 11 15.E-Learning to Build Problem Solving Skill
**Nov 13 16.Simulations and Games
**Nov 18 17.Applying the Guidelines
*Project Presentations 11-20 to 12-5
**Nov 20 Project Presentations
**Nov 25 Project Presentations
**Nov 27 Thanksgiving, no class
**Dec 2 Project Presentations
**Dec 4 Project Presentations
*Final Project due Dec 12

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 8-26 to 9-4''=====

*8-26 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment.docx|Examples (click to get)]] is due next Thursday, 9-5
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2013.docx|Project]] step 1 is due in 16 days on Thursday, 9-12
**NOTE: See reading assignment for next time on next date.

*8-28 E-learning intro and KLI Framework events [[Media:L01-e-learning-promises-pitfalls.pptx|(Click here for slides)]]
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings, b) associated quiz & c) discussion board post on Blackboard

*9-2 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
***Pre-class quiz: Answer questions for Chpt2 Quiz on Blackboard (5 minutes)
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-4 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
***Pre-class quiz: Answer questions for Chpt3 Quiz on Blackboard
**Reading: KLI sect 4-5 (12 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do two posts on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds by guest lecturer Vincent Aleven
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2 is due on 9-26
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do two posts (total) on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
***Posts: Do two posts (total) on the readings.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
*** Do the quiz and one post.

*9-23 Empirical CTA: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do two posts on the reading
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2.

*9-25 Contiguity Principle
**Reading: 5.Contiguity Principle (24 pages)
**Due: P2:Benchmark Tasks & Rational Cognitive Task Analysis
**Class activity: Peer review of P2

*9-30 From CTA to model building & instructional design
**Reading: [[Media:CS02-koedinger-terao-rev.pdf | Picture Algebra paper]]
**Class activity: Work on P3. How will you collect data?

*10-2 Modality Principle
**Reading: 6.Modality Principle (18 pages)
**Class activity: Work on P3. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)

*10-9 Flex topic: Design & Urban Legends Do Learners Really Know Best? Urban Legends in Education
**Reading: [http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
**Optional reading: [[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
**Due: P3: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals

*10-14 Richard Clark visit to class [Was previously Coherence Principle]
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf‎ | Clark_CTA_In_Healthcare_Chapter_2012.pdf‎]]
**See his talk on Monday at 3pm in 6115 Gates
**Do 2 posts and come prepared to ask him good questions

*10-16 Coherence and Personalization Principles
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)

=====Learning By Doing Principles 10-21 to 11-18 =====
*10-21 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quiz and one post

*10-23 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**DUE: P4: Assessment & Initial Instructional Design
**Assignment: P5 is due 11-7

*10-28 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)

*10-30 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)

*11-4 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)

*11-6 Who’s in Control?
**Reading: 14.Who’s in Control? 30 pages)

*11-11 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-26

*11-13 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)

*11-18 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)

=====Project Presentations 11-21 to 12-5=====

*11-21 Project Presentations
*11-26 Project Presentations
**Faculty course evaluation
**Changed "Due: P6: Research Design" to revise your project with a particular focus on improving steps 3 and 5. Turn all your revisions in as part of the final project and include the reflection statement (see the project assignment handout).
**Assignment: Final Project is due 12-13
*11-28 Thanksgiving, no class
*12-3 Project Presentations
*12-5 Project Presentations

=====Final Project Due on 12-13=====
*12-13 Project Due

E-Learning Design Principles and Methods 2016

2014-08-20T18:31:22Z

Jobodnar: /* Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16 */

==IN PROGRESS!!!!==
====Course Details====
Course number: 05-823

Semester: Fall 2014

Carnegie Mellon University

=====Class times=====
10:30 to 11:50 Tuesday & Thursday

=====Location=====
104 South Craig St (SCR)

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instruction.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in educational technology development.

====Flipped Homework: Reading Reports and Reading Quizzes====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am''' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Grading====

* 55% Final Project
** 45% Six parts of final project, 7.5% each
** 10% Final project submission
* 5% E-Learning examples assignment
* 10% Peer review and feedback
* 10% Pre-class quizzes & reading reports
* 10% Chapter summary
* 10% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 8-26 to 9-4
**Aug 26 Overview; Examples Assignment; Project
**Aug 28 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 2 2.How People Learn; KLI KC's
**Sept 4 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-9 to 9-16
**Sept 9 Determining instructional goals (tasks)
**Sept 11 Guest lecture
**Sept 16 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16
**Sept 18 4.Multi-media Principle
**Sept 23 Empirical Cognitive Task Analysis: Think aloud
**Sept 25 5.Contiguity Principle
**Sept 30 CTA: DFA & Model building
**Oct 2 6.Modality Principle & 7.Redundancy Principle
**Oct 7 CTA & Designing Assessments for Continual Improvement
**Oct 9 Midterm review; Flex topic
**Oct 14 8.Coherence Principle
**Oct 16 9.Personalization Principle
*Learning By Doing Principles 10-21 to 11-18
**Oct 21 10.Segmenting and Pretraining
**Oct 23 KLI & Selecting appropriate instructional principles
**Oct 28 11.Leveraging Examples in E-Learning
**Oct 30 12.Does Practice Make Perfect
**Nov 4 13.Learning Together Virtually
**Nov 6 14.Who’s in Control?
**Nov 11 15.E-Learning to Build Problem Solving Skill
**Nov 13 16.Simulations and Games
**Nov 18 17.Applying the Guidelines
*Project Presentations 11-20 to 12-5
**Nov 20 Project Presentations
**Nov 25 Project Presentations
**Nov 27 Thanksgiving, no class
**Dec 2 Project Presentations
**Dec 4 Project Presentations
*Final Project due Dec 12

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 8-26 to 9-4''=====

*8-26 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment.docx|Examples (click to get)]] is due next Thursday, 9-5
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2013.docx|Project]] step 1 is due in 16 days on Thursday, 9-12
**NOTE: See reading assignment for next time on next date.

*8-28 E-learning intro and KLI Framework events [[Media:L01-e-learning-promises-pitfalls.pptx|(Click here for slides)]]
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings, b) associated quiz & c) discussion board post on Blackboard

*9-2 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
***Pre-class quiz: Answer questions for Chpt2 Quiz on Blackboard (5 minutes)
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-4 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
***Pre-class quiz: Answer questions for Chpt3 Quiz on Blackboard
**Reading: KLI sect 4-5 (12 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do two posts on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds by guest lecturer Vincent Aleven
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2 is due on 9-26
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do two posts (total) on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
***Posts: Do two posts (total) on the readings.

=====Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
*** Do the quiz and one post.

*9-23 Empirical CTA: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do two posts on the reading
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2.

*9-25 Contiguity Principle
**Reading: 5.Contiguity Principle (24 pages)
**Due: P2:Benchmark Tasks & Rational Cognitive Task Analysis
**Class activity: Peer review of P2

*9-30 From CTA to model building & instructional design
**Reading: [[Media:CS02-koedinger-terao-rev.pdf | Picture Algebra paper]]
**Class activity: Work on P3. How will you collect data?

*10-2 Modality Principle
**Reading: 6.Modality Principle (18 pages)
**Class activity: Work on P3. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)

*10-9 Flex topic: Design & Urban Legends Do Learners Really Know Best? Urban Legends in Education
**Reading: [http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
**Optional reading: [[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
**Due: P3: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals

*10-14 Richard Clark visit to class [Was previously Coherence Principle]
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf‎ | Clark_CTA_In_Healthcare_Chapter_2012.pdf‎]]
**See his talk on Monday at 3pm in 6115 Gates
**Do 2 posts and come prepared to ask him good questions

*10-16 Coherence and Personalization Principles
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)

=====Learning By Doing Principles 10-22 to 11-19 =====
*10-22 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quiz and one post

*10-24 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**DUE: P4: Assessment & Initial Instructional Design
**Assignment: P5 is due 11-7

*10-29 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)

*10-31 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)

*11-5 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)

*11-7 Who’s in Control?
**Reading: 14.Who’s in Control? 30 pages)

*11-12 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-26

*11-14 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)

*11-19 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)

=====Project Presentations 11-21 to 12-5=====

*11-21 Project Presentations
*11-26 Project Presentations
**Faculty course evaluation
**Changed "Due: P6: Research Design" to revise your project with a particular focus on improving steps 3 and 5. Turn all your revisions in as part of the final project and include the reflection statement (see the project assignment handout).
**Assignment: Final Project is due 12-13
*11-28 Thanksgiving, no class
*12-3 Project Presentations
*12-5 Project Presentations

=====Final Project Due on 12-13=====
*12-13 Project Due

E-Learning Design Principles and Methods 2016

2014-08-20T18:28:25Z

Jobodnar: /* Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16 */

==IN PROGRESS!!!!==
====Course Details====
Course number: 05-823

Semester: Fall 2014

Carnegie Mellon University

=====Class times=====
10:30 to 11:50 Tuesday & Thursday

=====Location=====
104 South Craig St (SCR)

=====Instructor=====
Professor Ken Koedinger

Office: 3601 Newell-Simon Hall, Phone: 412-268-7667

Email: Koedinger@cmu.edu, Office hours by appointment

=====Course Prerequisites=====
To enroll you must either be in the Masters of Educational Technology and Applied Learning Science (METALS) or get the permission of the instruction.

=====Textbook and Readings=====
"E-Learning and the Science of Instruction: 3rd edition" by Ruth Colvin Clark and Richard E. Mayer.

Other readings will be assigned in class. See below.

=====Class URLs=====
Syllabus and useful links: [http://learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014 learnlab.org/research/wiki/index.php/E-learning_Design_Principles_2014]

For quizzes and reading reports go [http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard].

====Goals====
This course is about e-learning design principles, the evidence and theory behind them, and how to apply these principles to develop effective educational technologies. It is organized around the book "e-Learning and the Science of Instruction: Proven Guidelines for Consumers and Designers of Multimedia Learning" by Clark & Mayer with further readings drawn from cognitive science, educational psychology, and human-computer interaction. You will learn design principles 1) for combining words, audio, and graphics in multimedia instruction, 2) for combining examples, explanations, practice and feedback in online support for learning by doing, and 3) for balancing learner versus system control and supporting student metacognition. You will read about the experiments that support these design principles, see examples of how to design such experiments, and practice applying the principles in educational technology development.

====Flipped Homework: Reading Reports and Reading Quizzes====

You will have "flipped homework", a variation on the flipped classroom idea you might have heard of. Flipped homework is an assignment before a relevant class meeting rather than after it. It helps you to check your understanding of what you read, to practice to enhance your memory (we will talk about the "testing effect" in class), and to get a better sense of what you don't know so you are prepared to ask questions in class. It also helps instructors focus the class discussion to better avoid belaboring known points and pursue student needs and interests.

Before some class sessions, you will asked to do a quiz associated with the assigned book chapter. The quizzes will be on the Blackboard site ([http://www.cmu.edu/blackboard/ www.cmu.edu/blackboard], the course is listed as "Special Topics in HCI"). Before other class sessions, you will be asked to write "reading reports". We will use the discussion board on Blackboard. You should complete assigned quizzes or reading reports ''before 9am''' on the day of class.

For reading reports, the discussion forum post will usually direct you as to how to reply.
If not otherwise directed, you should make '''two posts''' on the readings. Your ''two'' posts may be original or in response to another post (one of both is nice).
*Original posts should contain one or more of the following:
**something you learned from the reading or slides
**a question you have about the reading or slides or about the topic in general
**a connection with something you learned or did previously in this or another course, or in other professional work or research
*Replies should be an on-topic, relevant response, clarification, or further comment on another student’s post.

In general, please come to class prepared to ask questions and give answers.

====Grading====

* 55% Final Project
** 45% Six parts of final project, 7.5% each
** 10% Final project submission
* 5% E-Learning examples assignment
* 10% Peer review and feedback
* 10% Pre-class quizzes & reading reports
* 10% Chapter summary
* 10% Class participation

====Class Schedule in Brief====
*E-Learning Introduction 8-26 to 9-4
**Aug 26 Overview; Examples Assignment; Project
**Aug 28 1.E-learning; KLI Framework events (The "1." indicates this is a chapter in the Clark & Mayer book)
**Sept 2 2.How People Learn; KLI KC's
**Sept 4 3.Evidence-based practice; KLI Learning & Instructional Events
*Instructional Goals and Cognitive Task Analysis 9-9 to 9-16
**Sept 9 Determining instructional goals (tasks)
**Sept 11 Guest lecture
**Sept 16 Discovering learning objectives (KCs) & Rational Cognitive Task Analysis
*Multimedia Principles and Cognitive Task Analysis 9-18 to 10-16
**Sept 18 4.Multi-media Principle
**Sept 23 Empirical Cognitive Task Analysis: Think aloud
**Sept 25 5.Contiguity Principle
**Sept 30 CTA: DFA & Model building
**Oct 2 6.Modality Principle & 7.Redundancy Principle
**Oct 7 CTA & Designing Assessments for Continual Improvement
**Oct 9 Midterm review; Flex topic
**Oct 14 8.Coherence Principle
**Oct 16 9.Personalization Principle
*Learning By Doing Principles 10-21 to 11-18
**Oct 21 10.Segmenting and Pretraining
**Oct 23 KLI & Selecting appropriate instructional principles
**Oct 28 11.Leveraging Examples in E-Learning
**Oct 30 12.Does Practice Make Perfect
**Nov 4 13.Learning Together Virtually
**Nov 6 14.Who’s in Control?
**Nov 11 15.E-Learning to Build Problem Solving Skill
**Nov 13 16.Simulations and Games
**Nov 18 17.Applying the Guidelines
*Project Presentations 11-20 to 12-5
**Nov 20 Project Presentations
**Nov 25 Project Presentations
**Nov 27 Thanksgiving, no class
**Dec 2 Project Presentations
**Dec 4 Project Presentations
*Final Project due Dec 12

====Class Schedule with Readings and Assignments====

'''NOTE:''' This section is "living" -- it will grow and change as the semester goes on.

=====''E-Learning Introduction 8-26 to 9-4''=====

*8-26 Overview, course project, your interests
**Class activity: Discuss your interests in e-learning
**Assignment: [[Media:edtech-example-review-assignment.docx|Examples (click to get)]] is due next Thursday, 9-5
***BRING two screen shots of your first example to ''next'' class
**Assignment: [[Media:E-learning-project-assignment-2013.docx|Project]] step 1 is due in 16 days on Thursday, 9-12
**NOTE: See reading assignment for next time on next date.

*8-28 E-learning intro and KLI Framework events [[Media:L01-e-learning-promises-pitfalls.pptx|(Click here for slides)]]
**Class activity: Promises & pitfalls review of e-learning examples
***BRING two screen shots of your first example to this class
**Reading (from course book): 1.e-Learning: Promise & Pitfalls (28 pages). [[Media:E-Learning-Ch1.pdf|You can get this chapter here this time]] but order the book right now!
***Pre-class quiz: Answer questions for Chpt1 Quiz on Blackboard
**For next time:
***BRING two screen shots of your ''second'' example to this class
***Review project step1 and come with a preliminary project idea. You might write some thoughts down, but you do not need to hand anything in.
***a) Do the two readings, b) associated quiz & c) discussion board post on Blackboard

*9-2 How People Learn and KLI Knowledge Components [[Media:L02-ELDP-how-people-learn.pptx|(Slides)]]
**Read Ch2.How Do People Learn from E-Courses (20 pages) [[Media:E-Learning-Ch2.pdf|You can get this chapter here this '''last''' time!]]
***Pre-class quiz: Answer questions for Chpt2 Quiz on Blackboard (5 minutes)
**Read [[Media:KLI-Framework-KoedingerCorbettPerfetti2012.pdf|KLI Framework paper]] sections 1-3 (18 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: KC type in e-learning examples
***BRING two screen shots of your ''second'' example to this class.
**Class activity: Project idea discussion
***Come prepared with a preliminary project idea

*9-4 Evidence-based practice and KLI Learning & Instructional Events [[Media: (Slides)]]
**Reading: 3.Evidence-based practice (18 pages)
***Pre-class quiz: Answer questions for Chpt3 Quiz on Blackboard
**Reading: KLI sect 4-5 (12 pages)
***Make one post to Blackboard -- see questions in Forum introduction
**Class activity: Principles present in e-learning examples
**DUE: [[Media:edtech-example-review-assignment.docx|Examples assignment]] is due at beginning of class. Please submit on blackboard.

=====Instructional Goals and Cognitive Task Analysis 9-9 to 9-16=====

*9-9 Goals, assessment tasks, cognitive task analysis, and instructional design
**Class activity: Review Project ideas and step 1 write-up requirements; consider assessment tasks
**Reading: [[Media:Feldon_Timmerman_etal_2010.pdf|Feldon paper]]
***Posts: Do two posts on the Feldon reading.

*9-11 Cognitive Task Analysis and Think Alouds by guest lecturer Vincent Aleven
**DUE: Project step P1: Domain, Context & Initial Resources
**Assignment: Project step P2 is due on 9-26
**Reading: [[Media:Lovett98.pdf|Lovett paper]] and [[Media:Gomoll-90.pdf|Gomoll paper]]
***Posts: Do two posts (total) on the Lovett and Gomoll papers.

*9-16 Discovering learning objectives (KCs) and Rational Cognitive Task Analysis
**Reading: [[Media:Zhu&Simon-1987.pdf|Zhu & Simon paper]]
***Posts: Do two posts (total) on the readings.

=====Multimedia Principles and Cognitive Task Analysis 9-19 to 10-17=====
*9-18 Multi-media Principle
**Reading: 4.Multi-media Principle (24 pages)
*** Do the quiz and one post.

*9-23 Empirical CTA: Difficulty Factors Assessment (DFA)
**Reading: [[Media:CogSci97-Heffernan-distrib.pdf‎ | Heffernan paper]]
***Do two posts on the reading
**Come with an attempt at a model of one your task solutions and, ideally, with an initial draft of project step 2.

*9-25 Contiguity Principle
**Reading: 5.Contiguity Principle (24 pages)
**Due: P2:Benchmark Tasks & Rational Cognitive Task Analysis
**Class activity: Peer review of P2

*9-30 From CTA to model building & instructional design
**Reading: [[Media:CS02-koedinger-terao-rev.pdf | Picture Algebra paper]]
**Class activity: Work on P3. How will you collect data?

*10-2 Modality Principle
**Reading: 6.Modality Principle (18 pages)
**Class activity: Work on P3. Analyzing your data

*10-7 Redundancy Principle & CTA via Data Mining
**Reading: 7.Redundancy Principle (18 pages)
**Reading: [[Media:Koedinger-et-al-aied2013.pdf | e-learning data to improvement]] (10 pages)

*10-9 Flex topic: Design & Urban Legends Do Learners Really Know Best? Urban Legends in Education
**Reading: [http://www.articulate.com/rapid-elearning/visual-graphic-design/ Visual & Graphic Design for e-learning blog]
**Optional reading: [[Media:Kirschner-Merrienboer-2013.pdf | Do Learners Really Know Best? Urban Legends in Education]]
**Due: P3: Empirical Cognitive Task Analysis & Cognitive Model of Instructional Goals

*10-14 Richard Clark visit to class [Was previously Coherence Principle]
**Reading: [[Media:Clark_CTA_In_Healthcare_Chapter_2012.pdf‎ | Clark_CTA_In_Healthcare_Chapter_2012.pdf‎]]
**See his talk on Monday at 3pm in 6115 Gates
**Do 2 posts and come prepared to ask him good questions

*10-16 Coherence and Personalization Principles
**Reading: 8.Coherence Principle (28 pages)
**Reading: 9.Personalization Principle (26 pages)

=====Learning By Doing Principles 10-22 to 11-19 =====
*10-22 Segmenting and Pretraining
**Reading: 10.Segmenting and Pretraining (18 pages)
**Do quiz and one post

*10-24 KLI & Selecting appropriate instructional principles
**Reading: KLI sections 6-7
**DUE: P4: Assessment & Initial Instructional Design
**Assignment: P5 is due 11-7

*10-29 Leveraging Examples in E-Learning
**Reading: 11.Leveraging Examples in E-Learning (28 pages)

*10-31 Does Practice Make Perfect
**Reading: 12.Does Practice Make Perfect (28 pages)

*11-5 Learning Together Virtually
**Reading: 13.Learning Together Virtually (30 pages)

*11-7 Who’s in Control?
**Reading: 14.Who’s in Control? 30 pages)

*11-12 Simulations and Games
**Reading: 16.Simulations and Games (32 pages)
**DUE: P5: Instructional Design Prototyping & Testing
**Assignment: P6 is due 11-26

*11-14 E-Learning to Build Problem Solving Skill
**Reading: 15.E-Learning to Build Problem Solving Skill (30 pages)

*11-19 Applying the Guidelines
**Reading: 17.Applying the Guidelines (24 pages)

=====Project Presentations 11-21 to 12-5=====

*11-21 Project Presentations
*11-26 Project Presentations
**Faculty course evaluation
**Changed "Due: P6: Research Design" to revise your project with a particular focus on improving steps 3 and 5. Turn all your revisions in as part of the final project and include the reflection statement (see the project assignment handout).
**Assignment: Final Project is due 12-13
*11-28 Thanksgiving, no class
*12-3 Project Presentations
*12-5 Project Presentations

=====Final Project Due on 12-13=====
*12-13 Project Due