1. Big Data Research in Undergraduate Education George Karypis Department of Computer Science & Engineering University of Minnesota

2. PREDICTING STUDENT’S PERFORMANCE IN COURSE ACTIVITIES

3. Background & Motivation Learning management systems (LMS) are now widely deployed and have become integral components in how universities teach their courses – distribute course material, discussion forums, wikis, online quizzes, assignment distribution & submission, online gradebook, etc. They provide a mechanism by which a student’s “engagement” in a course can potentially be observed. Research question: – Can we leverage LMS information to predict how well a student will perform in the course’s assignments? Accurate predictions can be used to develop “early warning” systems.

4. Task – Predict the grade that a student will achieve in a graded activity (quiz or assignment) based on information associated with the student’s prior performance, the course, and the student’s LMS interactions. Primary data – University of Minnesota’s Moodle installation. – Over 11,000 students and 800 courses. – Over 114,000 assignment submissions, 75,000 quiz submissions and 250,000 forum posts. Problem setting

5. Features Student performance-specific features: – cumulative GPA & cumulative grade in the course so far. Activity and course-specific features: – activity type, course level, and department. Moodle interaction features: – #of discussions initiated, #of posts-write, #of posts-reads, #of views, #of wiki adds, and #of other activities (e.g., surveys). – Counts were determined at different time intervals prior to the activity’s due date and covered only the period after the last graded activity.

6. Models – Baseline Linear regression predicted grade for student s on activity a feature vector for student’s s activity a estimated linear model

7. Models – Collaborative multi-regression Estimates multiple linear regression models with student-specific linear combinations. feature vector student-specific combination weight student and course bias terms k linear models

8. Collaborative Multi-Regression Models Learns a small number of models – Captures performance patterns of student groups. – Makes use of the similarities among the students (with respect to performance). Achieves personalization through – Student-specific bias terms. – Student-specific combination weights (memberships).

9. Results – Prediction accuracy

10. Results—Effect of bias terms

11. Results—Feature importance

12. A deeper look…

13. +Moodle features +assignments +GPA +quizzes

14. A deeper look… +Moodle features +assignments +GPA +quizzes

15. A deeper look…

16. Observations Using the Moodle interaction features leads to better prediction accuracy. Features mostly contributing to predicted grades relate to: – Viewing of course material – Previous performance Features related to viewing course material contribute to the predictions of some students more than others. – Some departments tend to have students whose viewing of course material does not contribute much to their predicted grades.