Learning about social learning in MOOCs: From statistical analysis to generative model

Christopher G. Brinton, Mung Chiang, Shaili Jain, Henry Lam, Zhenming Liu, Felix Ming Fai Wong

Research output: Contribution to journalArticle

128 Scopus citations

Abstract

We study user behavior in the courses offered by a major massive online open course (MOOC) provider during the summer of 2013. Since social learning is a key element of scalable education on MOOC and is done via online discussion forums, our main focus is on understanding forum activities. Two salient features of these activities drive our research: (1) high decline rate: for each course studied, the volume of discussion declined continuously throughout the duration of the course; (2) high-volume, noisy discussions: at least 30 percent of the courses produced new threads at rates that are infeasible for students or teaching staff to read through. Further, a substantial portion of these discussions are not directly course-related. In our analysis, we investigate factors that are associated with the decline of activity on MOOC forums, and we find effective strategies to classify threads and rank their relevance. Specifically, we first use linear regression models to analyze the forum activity count data over time, and make a number of observations; for instance, the teaching staff's active participation in the discussions is correlated with an increase in the discussion volume but does not slow down the decline rate. We then propose a unified generative model for the discussion threads, which allows us both to choose efficient thread classifiers and to design an effective algorithm for ranking thread relevance. Further, our algorithm is compared against two baselines using human evaluation from Amazon Mechanical Turk.

Original languageEnglish (US)
Article number6851916
Pages (from-to)346-359
Number of pages14
JournalIEEE Transactions on Learning Technologies
Volume7
Issue number4
DOIs
StatePublished - Oct 1 2014

All Science Journal Classification (ASJC) codes

  • Education
  • Engineering(all)
  • Computer Science Applications

Keywords

  • MOOC
  • concept learning
  • data mining
  • regression
  • social learning networks

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