  Predictive models of student success in Massive Open Online Courses (MOOCs)
are a critical component of effective content personalization and adaptive
interventions. In this article we review the state of the art in predictive
models of student success in MOOCs and present a categorization of MOOC
research according to the predictors (features), prediction (outcomes), and
underlying theoretical model. We critically survey work across each category,
providing data on the raw data source, feature engineering, statistical model,
evaluation method, prediction architecture, and other aspects of these
experiments. Such a review is particularly useful given the rapid expansion of
predictive modeling research in MOOCs since the emergence of major MOOC
platforms in 2012. This survey reveals several key methodological gaps, which
include extensive filtering of experimental subpopulations, ineffective student
model evaluation, and the use of experimental data which would be unavailable
for real-world student success prediction and intervention, which is the
ultimate goal of such models. Finally, we highlight opportunities for future
research, which include temporal modeling, research bridging predictive and
explanatory student models, work which contributes to learning theory, and
evaluating long-term learner success in MOOCs.
