Mining MOOC Clickstreams: Video-Watching Behavior vs. In-Video Quiz Performance

Christopher Greg Brinton, Swapna Buccapatnam, Mung Chiang, H. Vincent Poor

Research output: Contribution to journalArticle

34 Scopus citations

Abstract

Student video-watching behavior and quiz performance are studied in two Massive Open Online Courses (MOOCs). In doing so, two frameworks are presented 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, recurring subsequences of student behavior are extracted, which contain fundamental characteristics such as reflecting (i.e., repeatedly playing and pausing) and revising (i.e., plays and skip backs). It is found that some of these behaviors are significantly correlated with changes in the likelihood that a student will be Correct on First Attempt (CFA) or not in answering quiz questions, and in ways that are not necessarily intuitive. Then, with the position-based framework, models of quiz performance are devised based on positions visited in a video. In evaluating these models through CFA prediction, it is found that three of them can substantially improve prediction quality, which underlines the ability to relate this type of behavior to quiz scores. Since this prediction considers videos individually, these benefits also suggest that these models are useful in situations where there is limited training data, e.g., for early detection or in short courses.

Original languageEnglish (US)
Article number7440878
Pages (from-to)3677-3692
Number of pages16
JournalIEEE Transactions on Signal Processing
Volume64
Issue number14
DOIs
StatePublished - Jul 15 2016

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering

Keywords

  • Clickstream Data
  • Data Mining
  • Learning Analytics
  • MOOC
  • Performance Prediction
  • Social Learning Networks

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