Difference between revisions of "Educational Research Methods 2012"
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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.
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. students learn how to apply these methods to their own research programs.
Revision as of 16:06, 13 January 2012
Still under construction ... with remnants from 2010 version ...
- 1 Research Methods for the Learning Sciences 05-748
- 1.1 Class times
- 1.2 Location
- 1.3 Instructor
- 1.4 Class URL
- 1.5 Goals
- 1.6 Course Prerequisites
- 1.7 Textbook and Readings
- 1.8 Reading Reports and Pre-Class Preparation
- 1.9 Grading
- 1.10 Class Schedule in Brief
- 1.11 Class Schedule with Readings and Assignments
- 1.11.1 Course Intro & Formulating Good Research Questions (Koedinger)
- 1.11.2 Cognitive Task Analysis (Koedinger)
- 1.11.3 Video and Verbal Protocol Analysis (Lovett, Rosé)
- 1.11.4 Cognitive Task Analysis - Revisited (Koedinger)
- 1.11.5 Psychometrics, reliability, Item Response Theory (Junker)
- 1.11.6 Design Research & Qualitative Methods (Koedinger)
- 1.11.7 NO CLASS – Spring break
- 1.11.8 Surveys, Questionnaires, Interviews (Kiesler)
- 1.11.9 Educational data mining (Scheines, Koedinger)
- 1.11.10 Experimental Research Methods (Koedinger)
- 1.11.11 Wrap-up
Research Methods for the Learning Sciences 05-748
Spring 2012 Syllabus Carnegie Mellon University
4:30 to 5:50 Tuesday & Thursday
To be determined, probably 3001 Newell Simon Hall.
Professor Ken Koedinger
Office: 3601 Newell-Simon Hall
Office hours by appointment
Syllabus and useful links: learnlab.org/research/wiki/index.php/Educational_Research_Methods_2012
For reading reports: www.cmu.edu/blackboard
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.
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 www.atomicdogpublishing.com/BookDetails.asp?BookEditionID=160
Other readings will be assigned in class.
Reading Reports and Pre-Class Preparation
For more effective class meetings, students are asked to write "reading reports" before most class sessions. We will use the discussion board on 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 Assistment system, parts of the an 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.
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
- 20% Before-class preparation, including reading reports, and in-class participation
- 50% Assignments
- Project & final paper
- 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.
- Think of it as writing a grant proposal. 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. Since this is styled as 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.
- Final Project Milestones
- February 15 - a list of possible project ideas including the method(s) in email
- March 5 - a paragraph project proposal in email
- March 15 - approval of proposed project
- April 20 - turn in a draft for feedback
- April 26 - feedback provided
- Desired: set up a meeting to discuss draft with an instructor
- May 5 - final paper due
Class Schedule in Brief
- Course Intro & Formulating Good Research Questions: Jan 17 (T)
- Cognitive Task Analysis 1: Jan 19, 24, 26 (RTR)
- Video and Verbal Protocol Analysis: Jan 31, Feb 2,7,9,14,16 (TRTRTR)
- Guest Instructor(s): Marsha Lovett & Carolyn Rose
- Cognitive Task Analysis 2: Feb 21, 23 (TR)
- Educational Measurement & Psychometrics: Feb 28, Mar 1, 6 (TRT)
- Guest Instructor(s): Brian Junker
- Educational Design Research: Mar 8 (R)
- NO CLASS – Spring break, Mar 13, 15 (TR)
- Surveys, Questionnaires, Interviews: Mar 20, 22 (TR)
- Guest Instructor(s): Sara Kiesler
- Educational data mining: March 27, 29, Apr 3, 5, 10, 12 (TRTRTR)
- Guest Instructor(s): Richard Scheines (& Ken Koedinger)
- Experimental Methods: Apr 17, 24, 26, May 1 (TTRT)
- NO CLASS – Spring Carnival, Apr 19 (R)
- Wrap-up: May 3 (R)
Class Schedule with Readings and Assignments
NOTE: This is a "living" document. It carries over some elements from the past course offering that may get changed before the scheduled class period.
Course Intro & Formulating Good Research Questions (Koedinger)
- Assignment on Cognitive Task Analysis: Assignment2.doc
Cognitive Task Analysis (Koedinger)
- Zhu, X. & Simon, H. A. (1987). Learning mathematics from examples and by doing. Cognition and Instruction, 4(3), 137-166.
- Slides: CTA-01.pdf
- Lovett paper
- [Optional reading] 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).
- Clark, R. E., Feldon, D., van Merriënboer, J., Yates, K., & Early, S. (2007). Cognitive task analysis: In J. M. Spector, M. D. Merrill, J. J. G. van Merriënboer, & M. P. Driscoll (Eds.), Handbook of research on educational communications and technology (3rd ed., pp. 577–593). Mahwah, NJ: Lawrence Erlbaum Associates.
- Newell & Simon
Other possible readings:
- How People Learn Chapter 2: How Experts Differ From Novices
Video and Verbal Protocol Analysis (Lovett, Rosé)
- 1-31-12: In this introductory lecture, we will discuss the main steps of protocol analysis and what can be gained from the process. We will discuss these 2 readings in class.
- Ericsson, K. A., & Simon, H. A. (1993). Protocol Analysis: Verbal Reports as Data (Revised Edition, pp. xii-xv). Cambridge, MA: MIT Press. Media:E&SPreface.pdf
- Gilhooly, 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, 611-625. Media:Gilhooly.pdf
- 2-2-12 Protocol Analysis of Educational Discussions
- [Half the class will read this one]Veel, R. (1999). Language, knowledge and authority in school mathematics, in Francis Christie (Ed.) Pedagogy and the Shaping of Consciousness: Linguistics and Social Processes, Continuum. Media:PedagogyChapter_7.pdf
- [Half the class will read this one] Williams, G. (1999). The pedagogic device and the production of pedagogic discourse: a case example in early literacy education, in Francis Christie (Ed.) Pedagogy and the Shaping of Consciousness: Linguistics and Social Processes, Continuum. Media:PedagogyChapter_4.pdf
- [Optional] Martin, J. and White, P. R. (2005). The Language of Evaluation: Appraisal in English, Chapter 3, Palgrave. Media:Martin-WhiteChapter_3.pdf
- [Optional] Michaels et al., 2007 paper on Accountable Talk AccountableTalkPaper
- 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 7Media:VanSch7.pdf
- Kumar, R., Ai, H., and Rosé (submitted). Choosing Optimal Levels of Social Interaction – Towards creating Human-like Conversational Tutors, submitted to the Intelligent Tutoring Systems ConferenceITS2010-Kumar
- Data set from Kumar et al. study Data
- Iris's coding manual Manual
- Chapter with alternative presentation of Reasoning coding Chapter
- 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, submitted to the International Journal of Computer Supported Collaborative Learning []
- Ai, H., Kumar, R., Nagasunder, A., Rose, C. P. (submitted). Exploring the Effectiveness of Social Capabilities and Goal Alignment in Computer Supported Collaborative Learning, submitted to the Intelligent Tutoring Systems ConferenceMedia:Its2010_submission_172.pdf
- Hua Ai's Lecture File:EduMethod.ppt
- Schooler, J. W., Ohlsson, S., & Brooks, K. (1993). Thoughts Beyond Words: When Language Overshadows Insight, Journal of Experimental Psychology 122(2), pp 166-183. Media:Schooleretal.pdf
- Download SIDE and the SIDE User's Manual from the webpage. []
Cognitive Task Analysis - Revisited (Koedinger)
- Koedinger & Nathan
- 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.
- Heffernan, N. & Koedinger, K. R. (1997). The composition effect in symbolizing: The role of symbol production vs. text comprehension: In Shafto, M. G. & Langley, P. (Eds.) Proceedings of the Nineteenth Annual Conference of the Cognitive Science Society, (pp. 307-312). Hillsdale, NJ: Erlbaum.
Psychometrics, reliability, Item Response Theory (Junker)
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. 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 . 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.
We will discuss on Tue whatever of this we can get through, and continue the discussion as needed on Thursday. There will be additional readings for Thu to introduce fitting and using IRT models.
1. Short introduction to R (Rintro.pdf)
Please download and install R for your computer (windows, mac or linux) from . Then try all the things in sections 1-9 of this handout (section 10 is optional). You can try most things by copying from the pdf and pasting into the R command window. You do not have to be completely done with this by the time we meet for lecture 2, but you should aim to finish it soon afterwards.
2. "Cognitive Assessment Models with Few Assumptions..." by Junker & Sijtsma (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. "Psychometric Principles in Student Assessment" by Mislevy et al (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.
- 3-6-12 Psychometrics continued
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). PDF
NO CLASS – Spring break
Surveys, Questionnaires, Interviews (Kiesler)
- Reading: Trochim Ch 4 and 5
- 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 (Scheines, Koedinger)
- 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
- 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
- Assignment: Due Thursday, 4/1. DOC
- Register an account on DataShop ( http://www.pslcdatashop.org ) and watch this video.
- Reading: Cen, H., Koedinger, K. R., & Junker, B. (2006). Learning Factors Analysis: A general method for cognitive model evaluation and improvement. In M. Ikeda, K. D. Ashley, T.-W. Chan (Eds.) Proceedings of the 8th International Conference on Intelligent Tutoring Systems, 164-175. Berlin: Springer-Verlag. PDF
- 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
- 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 No Summary Required.
- Do Unit 2 in the OLI course Empirical Research Methods
-- go to: http://oli.web.cmu.edu/openlearning/ -- go to Empirical Research methods (on left tab) -- click on Peek In -- complete Unit 2
- Read 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. PDF
- 4-10-12 Continuation of discussion of TETRAD
- 4-12-12 Continuation of discussion of TETRAD
Experimental Research Methods (Koedinger)
- 4-19-12 NO CLASS – Spring Carnival
- Reading: Trochim Ch 14
- Assignment 1 due before class
- Try Regression and ANOVA modules of OLI Statistics course
- 5-3-12 Course wrap-up