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Big data repositories from online learning platforms such as Massive OpenOnline Courses (MOOCs) represent an unprecedented opportunity to advanceresearch on education at scale and impact a global population of learners. Todate, such research has been hindered by poor reproducibility and a lack ofreplication, largely due to three types of barriers: experimental, inferential,and data. We present a novel system for large-scale computational research, theMOOC Replication Framework (MORF), to jointly address these barriers. Wediscuss MORF's architecture, an open-source platform-as-a-service (PaaS) whichincludes a simple, flexible software API providing for multiple modes ofresearch (predictive modeling or production rule analysis) integrated with ahigh-performance computing environment. All experiments conducted on MORF useexecutable Docker containers which ensure complete reproducibility whileallowing for the use of any software or language which can be installed in thelinux-based Docker container. Each experimental artifact is assigned a DOI andmade publicly available. MORF has the potential to accelerate and democratizeresearch on its massive data repository, which currently includes over 200MOOCs, as demonstrated by initial research conducted on the platform. We alsohighlight ways in which MORF represents a solution template to a more generalclass of problems faced by computational researchers in other domains.

MORF: A Framework for Predictive Modeling and Replication At Scale With Privacy-Restricted MOOC Data