ELBA: Exceptional Learning Behavior Analysis

Behavioral records collected through course assessments, peer assignments, and programming assignments in Massive Open Online Courses (MOOCs) provide multiple views about a student's study style. Study behavior is correlated with whether or not the student can get a certificate or drop out from a course. It is of predominant importance to identify the particular behavioral patterns and establish an accurate predictive model for the learning results, so that tutors can give well-focused assistance and guidance on specific students. However, the behavioral records of individuals are usually very sparse; behavioral records between individuals are inconsistent in time and skewed in contents. These remain big challenges for the state-of-the-art methods. In this paper, we engage the concept of subgroup as a trade-off to overcome the sparsity of individual behavioral records and inconsistency between individuals. We employ the framework of Exceptional Model Mining (EMM) to discover exceptional student behavior. Various model classes of EMM are applied on dropout rate analysis, correlation analysis between length of learning behavior sequence and course grades, and passing state prediction analysis. Qualitative and quantitative experimental results on real MOOCs datasets show that our method can discover significantly interesting learning behavioral patterns of students