Predicting Students’ Performance and Problem Solving Behavior from iList Log Data

Abstract

In this paper, we analyze data gathered from students’ interactions with iList, an intelligent tutoring system that teaches linked lists to computer science (CS) undergraduates. A number of features have been extracted from the log files which were used to; a) build predictive models of students’ performance, b) analyze temporal aspects of students’ problem solving behavior. Our results suggest that it is possible to build predictive models of performance with an accuracy of 87% by using logistic regression. The results also show that it is more likely a student will perform a step correctly if s/he spends more time on it.