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

TL;DR

MORF is an open-source platform that enables scalable, reproducible research on MOOC data by integrating flexible APIs, containerization, and high-performance computing, thereby overcoming barriers to replication and advancing educational research.

Contribution

The paper introduces MORF, a novel framework that facilitates large-scale, reproducible research on MOOC data through containerization and a flexible API, addressing experimental, inferential, and data barriers.

Findings

MORF includes over 200 MOOCs in its repository.

Initial research on MORF demonstrates its effectiveness in accelerating education research.

All experiments are reproducible via Docker containers with assigned DOIs.

Abstract

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.