2015/03/22 by Christopher G. Brinton, Swapna Buccapatnam, Brinton, Christopher G. +6 · 19 citations
Computer Science · Decision Sciences · #Advanced Bandit Algorithms Research #Artificial intelligence #Computer science #Data Stream Mining Techniques #Event (particle physics) #FOS: Computer and information sciences #Machine learning #Massive open online course #Online Learning and Analytics #Position (finance) #Quality (philosophy) #Sequence (biology) #Social and Information Networks (cs.SI) #World Wide Web #cs.SI
paper · pdf · doi:10.48550/arxiv.1503.06489
published in arXiv (Cornell University) (Cornell University)
openalex publication_date 2015/03/22 · arxiv created 2015/10/03 · arxiv updated 2015/10/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We study student behavior and performance in two Massive Open Online Courses (MOOCs). In doing so, we present two frameworks by which video-watching clickstreams can be represented: one based on the sequence of events created, and another on the sequence of positions visited. With the event-based framework, we extract recurring subsequences of student behavior, which contain fundamental characteris- tics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). We find that some of these behaviors are significantly associated with whether a user will be Correct on First Attempt (CFA) or not in answering quiz questions. With the position-based framework, we then devise models for performance. In evaluating these through CFA prediction, we find that three of them can substantially improve prediction quality in terms of accuracy and F1, which underlines the ability to relate behavior to performance. Since our prediction considers videos individually, these benefits also suggest that our models are useful in situations where there is limited training data, e.g., for early detection or in short courses.