Studying MOOC completion at scale using the MOOC replication framework

Research on learner behaviors and course completion within Massive Open Online Courses (MOOCs) has been mostly confined to single courses, making the findings difficult to generalize across different data sets and to assess which contexts and types of courses these findings apply to. This paper reports on the development of the MOOC Replication Framework (MORF), a framework that facilitates the replication of previously published findings across multiple data sets and the seamless integration of new findings as new research is conducted or new hypotheses are generated. In the proof of concept presented here, we use MORF to attempt to replicate 15 previously published findings across 29 iterations of 17 MOOCs. The findings indicate that 12 of the 15 findings replicated significantly across the data sets. Results contradicting previously published findings were found in two cases. MORF enables larger-scale analysis of MOOC research questions than previously feasible, and enables researchers around the world to conduct analyses on huge multi-MOOC data sets without having to negotiate access to data.