Identifying Latent Study Habits by Mining Learner Behavior Patterns in Massive Open Online Courses.

ABSTRACTMOOCs attract diverse users with varying habits. Identifying those patterns through clickstream analysis could enable more effective personalized support for student information seeking and learning in that online context. We propose a novel method to characterize types of sessions in MOOCs by mining the habitual behaviors of students within individual sessions. We model learning sessions as a distribution of activities and activity sequences with a topical N-gram model. The representation offers insights into what groupings of habitual student behaviors are associated with higher or lower success in the course. We also investigate how context information, such as time of day or a user's demographic information, is associated with the types of learning sessions.