MORF: A Post-Mortem
Year: 2025
Type: conference-paper
Abstract: There has been increasing interest in data enclaves in recent years, both in education and other fields. Data enclaves make it possible to conduct analysis on large-scale and higher-risk data sets, while protecting the privacy of the individuals whose data is included in the data sets, thus mitigating risks around data disclosure. In this article, we provide a post-mortem on the MORF (MOoc Replication Framework) 2.1 infrastructure, a data enclave expected to sunset and be replaced in the upcoming years, reviewing the core factors that reduced its usefulness for the community. We discuss challenges to researchers in terms of usability, including challenges involving learning to use core technologies, working with data that cannot be directly viewed, debugging, and working with restricted outputs. Our post-mortem discusses possibilities for ways that future infrastructures could get past these challenges.
Authors: Ryan Baker, Stephen Hutt
Institutions: University of Pennsylvania, University of Denver
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4407841569