MORF: A Post-Mortem

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.

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Publication Details

Published
2025-02-21
DOI
https://doi.org/10.1145/3706468.3706478
Citations
1
Primary Topic
COVID-19 diagnosis using AI
Type
article
Field-Weighted Citation Impact
1.36
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article

MORF: A Post-Mortem

Stephen Hutt, Ryan S. Baker
1 citations
COVID-19 diagnosis using AI
1.36
article

MORF: A Post-Mortem

Stephen Hutt, Ryan S. Baker
article en
1 citations

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.

University of Denver (US), University of Pennsylvania (US), Philadelphia University (US)
Openalex Percentile: Top 23%
COVID-19 diagnosis using AI
1.36
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