Evaluation of group fairness measures in student performance prediction problems

TL;DR

This paper evaluates various group fairness measures in student performance prediction models, highlighting the importance of measure choice and grade thresholds in achieving fair educational outcomes.

Contribution

It provides a comparative analysis of fairness measures in educational data mining, addressing a gap in evaluating fairness-aware models.

Findings

Fairness measure choice significantly impacts model fairness

Grade thresholds influence fairness outcomes

Different datasets exhibit varying fairness measure effectiveness

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.