Uncovering Hidden Engagement Patterns for Predicting Learner Performance in MOOCs
Hal Daumé III
ACM Conference on Learning at Scale (L@S'14). Works-in-Progress paper., 2014
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.
"Arti Ramesh and
Dan Goldwasser and
Burt Huang and
Hal Daumé III and
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"