Behavior Prediction in MOOCs using Higher Granularity Temporal Information

Abstract

In this paper, we present early research evaluating the predictive power of a variety of temporal features across student subpopulations with distinctive behaviors at the beginning of the course. Initial results illustrate that these features predict important differences across the subpopulations and over time in the courses. Ultimately, these results have implications for effectively targeting adaptive scaffolding tailored to the particular intentions and goals of subpopulations in MOOCs.