Abstract
The COVID-19 pandemic prompted widespread school closures and a swift transition to distance learning, raising concerns about consequences for student success. This study explores academic outcomes in STEM (Science, Technology, Engineering, and Mathematics) courses during COVID-induced distance learning using student-course records at an U.S. public university. We particularly focus on students from underserved populations and who have below average academic outcomes. Employing an expected shortfall regression strategy, we examine outcomes in the bottom quintile and compare differences between students whose household income were in the lowest bracket or without a parent holding a four-year college degree and peers for whom this was not the case, in distance and in-person learning. We show that during distance learning, students in disadvantaged populations in the lowest quintile had average grade differences of 0.11 and 0.06 points, respectively. We also find that targeted instructional changes in an introductory physics course were associated with narrower achievement gaps. These results suggest that while distance learning posed challenges for underserved students, deliberate strategies to increase interaction may potentially support greater equity in STEM education.
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Data availability
Currently, the LARC Data Set is available to University of Michigan (U-M) investigators who have obtained Institutional Review Board (IRB) approval and signed a memorandum of understanding describing their research interests. Investigators not affiliated with the University of Michigan must adhere to the established data request protocols of the University of Michigan to gain access to data. Investigators should contact student.data.request@umich.edu to request access to data. The expected time-frame for response to access requests depends on the the scope of the request. For accepted data requests, the timeline for data availability will be outlined in the data user agreement. More information about accessing the data can be found here: https://enrollment.umich.edu/data/learning-analytics-data-architecture-larc. Cleaned data are available from the authors upon university approval for LARC data. Individuals interested in obtaining this data should follow above steps to obtain approval from the University of Michigan. Source data are provided with this paper.
Code availability
Code to perform the expected shortfall regression method is available in the Supplementary Code.
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Research in this publication is partially supported by National Science Foundation (NSF) CAREER DMS-2238428 (K.M.T.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
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Man, R., Li, J. & Tan, K.M. Educational disparities in STEM during COVID-induced distance learning and a potential strategy to address them. Nat Commun (2026). https://doi.org/10.1038/s41467-026-69925-9
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DOI: https://doi.org/10.1038/s41467-026-69925-9
