Student Success Prediction in MOOCs

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 dual categorization of MOOC research according to both predictors (features) and prediction (outcomes). We critically survey work across each category, providing data on the data source, feature extraction from raw data, statistical modeling, model evaluation, prediction architecture, experimental subpopulations, and prediction outcome. 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 and research bridging predictive and explanatory student models.