Designing for Fairness in Higher Education Early WarningSystems
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by Kelly McConvey and Shion Guha
Published onMay 27, 2024
Designing for Fairness in Higher Education Early WarningSystems
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Abstract
Early Warning Systems are algorithms trained to predict the probability of a student graduating successfully. Despite the growing reliance on algorithms by Higher Education Institutions and significant interest in the development of these algorithms by the research community, there remain several critical questions related to the fairness of Early Warning System algorithms. In this case study, we assessed a model built on the Open University Learning Analytics dataset (OULAD) for gender and disability-related performance disparities. We found that our model displayed significant disparities between classes, indicating a higher likelihood of unnecessary interventions for women and individuals with recognized disabilities; and a lower likelihood of receiving interventions when needed. We then tested two mitigation approaches and found that a reductions approach is a viable solution for mitigating unfairness in Early Warning System.
Creative Commons Attribution 4.0 International License (CC-BY 4.0)