Abstract
Predicting students’ academic performance is one of the key tasks of educational data mining (EDM). Traditionally, the high forecasting quality of such models was deemed critical. More recently, the issues of fairness and discrimination w.r.t. protected attributes, such as gender or race, have gained attention. Although there are several fairness-aware learning approaches in EDM, a comparative evaluation of these measures is still missing. In this paper, we evaluate different group fairness measures for student performance prediction problems on various educational datasets and fairness-aware learning models. Our study shows that the choice of the fairness measure is important, likewise for the choice of the grade threshold.
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Notes
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The number of citations is reported by Google Scholar on \(1^{st}\) August 2022.
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We use the abbreviations of the fairness measures and datasets in Fig. 7.
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Acknowledgments
The work of the first author is supported by the Ministry of Science and Culture of Lower Saxony, Germany, within the Ph.D. program “LernMINT: Data-assisted teaching in the MINT subjects”. The work of the second author is funded by the German Research Foundation (DFG Grant NI-1760/1-1), project “Managed Forgetting”.
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Le Quy, T., Nguyen, T.H., Friege, G., Ntoutsi, E. (2023). Evaluation of Group Fairness Measures in Student Performance Prediction Problems. In: Koprinska, I., et al. Machine Learning and Principles and Practice of Knowledge Discovery in Databases. ECML PKDD 2022. Communications in Computer and Information Science, vol 1752. Springer, Cham. https://doi.org/10.1007/978-3-031-23618-1_8
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