Detecting Trait versus Performance Student Behavioral Patterns Using Discriminative Non-Negative Matrix Factorization

Recent studies have shown that students follow stable behavioral patterns while learning in online educational systems. These behavioral patterns can further be used to group the students into different clusters. However, as these clusters include both high-and low-performance students, the relation between the behavioral patterns and student performance is yet to be clarified. In this work, we study the relation between students' learning behaviors and their performance, in a self-organized online learning system that allows them to freely practice with various problems and worked examples. We represent each student's behavior as a vector of high-support sequential micro-patterns. Assuming that some behavioral patterns are shared across high-and low-performance students, and some are specific to each group, we group the students according to their performance. Having this assumption, we discover both the prevalent behavioral patterns in each group, and the shared patterns across groups using discriminative non-negative matrix factorization. Our experiments show that there are such common and specific patterns in students' behavior that are discriminative among students with different performances.