Uncovering hidden engagement patterns for predicting learner performance in MOOCs

  • Authors:

  • Arti Ramesh;Dan Goldwasser;Bert Huang;Hal Daume, III;Lise Getoor

  • Affiliations:

  • University of Maryland, College Park, College Park, MD, USA;University of Maryland, College Park, College Park, MD, USA;University of Maryland, College Park, College Park, MD, USA;University of Maryland, College Park, College Park, MD, USA;University of California, Santa Cruz, Santa Cruz, CA, USA

  • Venue:
  • Proceedings of the first ACM conference on Learning @ scale conference
  • Year:
  • 2014

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Abstract

Maintaining and cultivating student engagement is a prerequisite for MOOCs to have broad educational impact. Understanding student engagement as a course progresses helps characterize student learning patterns and can aid in minimizing dropout rates, initiating instructor intervention. In this paper, we construct a probabilistic model connecting student behavior and class performance, formulating student engagement types as latent variables. We show that our model identifies course success indicators that can be used by instructors to initiate interventions and assist students.