  Big data repositories from online learning platforms such as Massive Open
Online Courses (MOOCs) represent an unprecedented opportunity to advance
research on education at scale and impact a global population of learners. To
date, such research has been hindered by poor reproducibility and a lack of
replication, largely due to three types of barriers: experimental, inferential,
and data. We present a novel system for large-scale computational research, the
MOOC Replication Framework (MORF), to jointly address these barriers. We
discuss MORF's architecture, an open-source platform-as-a-service (PaaS) which
includes a simple, flexible software API providing for multiple modes of
research (predictive modeling or production rule analysis) integrated with a
high-performance computing environment. All experiments conducted on MORF use
executable Docker containers which ensure complete reproducibility while
allowing for the use of any software or language which can be installed in the
linux-based Docker container. Each experimental artifact is assigned a DOI and
made publicly available. MORF has the potential to accelerate and democratize
research on its massive data repository, which currently includes over 200
MOOCs, as demonstrated by initial research conducted on the platform. We also
highlight ways in which MORF represents a solution template to a more general
class of problems faced by computational researchers in other domains.
