  The use of machine learning techniques has expanded in education research,
driven by the rich data from digital learning environments and institutional
data warehouses. However, replication of machine learned models in the domain
of the learning sciences is particularly challenging due to a confluence of
experimental, methodological, and data barriers. We discuss the challenges of
end-to-end machine learning replication in this context, and present an
open-source software toolkit, the MOOC Replication Framework (MORF), to address
them. We demonstrate the use of MORF by conducting a replication at scale, and
provide a complete executable container, with unique DOIs documenting the
configurations of each individual trial, for replication or future extension at
https://github.com/educational-technology-collective/fy2015-replication. This
work demonstrates an approach to end-to-end machine learning replication which
is relevant to any domain with large, complex or multi-format,
privacy-protected data with a consistent schema.
