Replicating MOOC predictive models at scale

Abstract

We present a case study in predictive model replication for student dropout in Massive Open Online Courses (MOOCs) using a large and diverse dataset (133 sessions of 28 unique courses offered by two institutions). This experiment was run on the MOOC Replication Framework (MORF), which makes it feasible to fully replicate complex machine learned models, from raw data to model evaluation. We provide an overview of the MORF platform architecture and functionality, and demonstrate its use through a case study. In this replication of [41], we contextualize and evaluate the results of the previous work using statistical tests and a more effective model evaluation scheme. We find that only some of the original findings replicate across this larger and more diverse sample of MOOCs, with others replicating significantly in the opposite direction. Our analysis also reveals results which are highly relevant to the prediction task which were not reported in the original experiment. This work demonstrates the importance of replication of predictive modeling research in MOOCs using large and diverse datasets, illuminates the challenges of doing so, and describes our freely available, open-source software framework to overcome barriers to replication.