Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs

Arti Ramesh     Dan Goldwasser     Burt Huang     Hal Daumé III     Lisa Getoor    
ACM Conference on Learning at Scale (L@S'14). Works-in-Progress paper., 2014
[pdf]

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.


Bib Entry

  @InProceedings{RGHDG_las_2014,
    author = "Arti Ramesh and Dan Goldwasser and Burt Huang and Hal Daumé III and Lisa Getoor",
    title = "Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs",
    booktitle = "ACM Conference on Learning at Scale (L@S'14). Works-in-Progress paper.",
    year = "2014"
  }