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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 relationship 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 highsupport sequential micro-patterns. Then, 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 we can successfully detect such common and specific patterns in students’ behavior that can be further interpreted into student learning behavior trait patterns and performance patterns. 

Recent studies of student problem-solving behavior have shown stable behavior patterns within student groups. In this work, we study patterns of student behavior in a richer self-organized practice context where student worked with a combination of problems to solve and worked examples to study. We model student behavior in the form of vectors of micro-patterns and examine student behavior stability in various ways via these vectors. To discover and examine global behavior patterns associated with groups of students, we cluster students according to their behavior patterns and evaluate these clusters in accordance with student performance.