MOOCex: Exploring Educational Video via Recommendation

Massive Open Online Course (MOOC) platforms have scaled online education to unprecedented enrollments, but remain limited by their predetermined curricula. Increasingly, professionals consume this content to augment or update specific skills rather than complete degree or certification programs. To better address the needs of this emergent user population, we describe a visual recommender system called MOOCex. The system recommends lecture videos across multiple courses and content platforms to provide a choice of perspectives on topics of interest. The recommendation engine considers both video content and sequential inter-topic relationships mined from course syllabi. Furthermore, it allows for interactive visual exploration of the semantic space of recommendations within a learner's current context.