Flexible Learning with Semantic Visual Exploration and Sequence-Based Recommendation of MOOC Videos

Abstract

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their rigid, predetermined curricula. To overcome this limitation, this paper contributes a visual recommender system called MOOCex. The system recommends lecture videos across different courses by considering both video contents and sequential inter-topic relationships mined from course syllabi; and more importantly, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context. When compared to traditional methods (e.g., content-based recommendation and ranked list representations), MOOCex suggests videos from more diverse perspectives and helps learners make better video playback decisions. Further, feedback from MOOC learners and instructors indicates that the system enhances both learning and teaching effectiveness.