Educational Research Methods 10
Course page ... Research Methods for the Learning Sciences 85-748 Spring 2008 Syllabus Carnegie Mellon University
General Information Class times: 4:30 to 5:50 Tuesday & Thursday in NSH 3001
Instructors: Professor Kenneth R. Koedinger Office hours by appointment Location: 3601 Newell-Simon Hall Phone: 8-7667 Email: Koedinger@cmu.edu
Dr. Philip I. Pavlik Jr. Office hours by appointment Location: 300S Craig St, 224 Phone: 8-1618 Email: email@example.com
Class URL: TBD
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 overview of methods, cognitive task analysis, qualitative methods, protocol and discourse analysis, and educational data mining and log analysis. A key goal is to help students think about and learn how to apply these methods to their own research programs.
Course Prerequisites To enroll you must have taken 85-738, "Educational Goals, Instruction, and Assessment" or get the permission of the instruction.
Readings Textbook: "The Research Methods Knowledge Base: 3rd edition" by William M.K. Trochim and James P. Donnelly Other readings will be assigned in class.
Reading Reports Students are required to submit at least two posts per week to the course discussion group/blog before class, on either Sunday/Monday/Tuesday before class (for readings due Tuesday) or on Tuesday/Wednesday/Thursday before class (for readings due Thursday). These posts can be about 1) a question you had about the reading, something important you did not understand 2) an idea inspired by the reading 3) an interesting connection with something you learned or did previously in this or another course, or in other professional work or research 4) an on-topic, relevant response, clarification, or further comment on another student’s post
Grading In order to make the grading as systematic and equitable as possible, we are using a point system in which each homework assignment and the exams are given a certain number of points. Points associated with each assignment are listed below.
|Homework||row 1, cell 3|
|1. Tutor Evaluation||30|
|2. Addition Pseudo Tutor||40|
|3. Tutor Modification Task1||40|
|4. Tutor Modification Task2(1)||70|
|5. Think Aloud & Difficulty Factors Assessment(1)||90|
- These are pair or group assignments.
- Note, 5% of your course grade will be based on the instructors’ judgment of your level of learning , whether you have learned more or less than is reflected in the points you have earned. This judgment will be influenced by your class and group participation. Thus, regular attendance and active participation in group and class meetings is in your interest.
Converting group grades to individual grades Group homework grades are closely tied to individual grade. For example, if the group grade is 25 points, each individual member is likely to receive 25 points. However, to avoid rewarding free riding, distinctions will be made among individuals. We will not have adequate personal information to distinguish among group members for group work. Therefore in assigning individual grades, private evaluations from all group members will be solicited at the end of the course. These evaluations will take the form of a questionnaire asking each group member to allocate, among 100% of the total effort, the responsibility among the members for the group's homework assignments.
Class Schedule for Research Methods for the Learning Sciences Spring 2008 (Topics continue into blanks!)
1-13 Basic Research & Experimental Methods (Koedinger, Pavlik) 1-15 1-20 1-22 1-27 Cognitive Task Analysis (Koedinger, Pavlik) 1-29 2-3 2-5 Video and Verbal Protocol Analysis (Lovett, Rosé) 2-10 2-12 2-17 2-19 2-24 2-26 Ethnography & Design Experiments? 3-3 Surveys, Questionnaires, Interviews (Kiesler) 3-5 3-10 NO CLASS – Spring break 3-12 NO CLASS – Spring break 3-17 Psychometrics, reliability, Item Response Theory (Junker, Koedinger) 3-19 3-24 3-26 Educational data mining (Scheines, Pavlik) 3-31 4-2 4-7 4-9 4-14 4-16 NO CLASS – Spring Carnival 4-21 Cognitive Task Analysis - Revisited (Koedinger, Pavlik) 4-23 4-28 Wrap-up 4-30 Wrap-up