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The MOOC Replication Framework (MORF) is a framework that facilitates the replication of previously published findings across multiple data sets. It facilitates the construction and evaluation of end-to-end pipelines from raw data to evaluation. MORF is designed to ensure the seamless integration of new findings as new research is conducted or new hypotheses are generated, and to support the generation of novel research in the learning sciences.

MORF is a joint project between multiple research laboratories, with primary implementation in recent years occurring at the University of Pennsylvania Center for Learning Analytics, the etc lab at the University of Michigan School of Information, and the Human-Computer Interaction Institute at Carnegie Mellon University. Other universities also have instances of MORF.

MORF has now partnered with the ASSISTments E-TRIALS infrastructure to create RAILKaM, an integration where researchers will be able to link MOOC and intelligent tutor data.[1]


Data Studies

Hutt et al. (2022)[2]

Title - Controlled outputs, full data: A privacy-protecting infrastructure for MOOC data.



  2. Hutt, S., Baker, R. S., Ashenafi, M. M., Andres‐Bray, J. M., & Brooks, C. (2022). Controlled outputs, full data: A privacy‐protecting infrastructure for MOOC data. British Journal of Educational Technology.