# Evaluation of group fairness measures in student performance prediction problems

arXiv: https://arxiv.org/abs/2208.10625 · HTML: https://mlsanity.com/paper/2208.10625/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 40 references that are in the map.
- First posted on arXiv 22 Aug 2022.

## Builds on (most cited first, 13 of 13)

- [Equality of Opportunity in Supervised Learning](https://mlsanity.com/paper/1610.02413/) (2016)
- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Fair prediction with disparate impact: A study of bias in recidivism prediction instruments](https://mlsanity.com/paper/1610.07524/) (2016)
- [A Reductions Approach to Fair Classification](https://mlsanity.com/paper/1803.02453/) (2018)
- [Retiring Adult: New Datasets for Fair Machine Learning](https://mlsanity.com/paper/2108.04884/) (2021)
- [Algorithmic decision making and the cost of fairness](https://mlsanity.com/paper/1701.08230/) (2017)
- [Fairness in Criminal Justice Risk Assessments: The State of the Art](https://mlsanity.com/paper/1703.09207/) (2017)
- [A survey on datasets for fairness-aware machine learning](https://mlsanity.com/paper/2110.00530/) (2021)
- [50 Years of Test (Un)fairness: Lessons for Machine Learning](https://mlsanity.com/paper/1811.10104/) (2018)
- [On the relation between accuracy and fairness in binary classification](https://mlsanity.com/paper/1505.05723/) (2015)
- [AdaFair: Cumulative Fairness Adaptive Boosting](https://mlsanity.com/paper/1909.08982/) (2019)
- [Towards Equity and Algorithmic Fairness in Student Grade Prediction](https://mlsanity.com/paper/2105.06604/) (2021)
- [AI and Holistic Review: Informing Human Reading in College Admissions](https://mlsanity.com/paper/1912.09318/) (2019)

## Built on by (most cited first, 5 of 5)

- [Fairness of ChatGPT](https://mlsanity.com/paper/2305.18569/) (2023)
- [FairAIED: Navigating Fairness, Bias, and Ethics in Educational AI Applications](https://mlsanity.com/paper/2407.18745/) (2024)
- [No Free Lunch: Retrieval-Augmented Generation Undermines Fairness in LLMs, Even for Vigilant Users](https://mlsanity.com/paper/2410.07589/) (2024)
- [A review of clustering models in educational data science towards fairness-aware learning](https://mlsanity.com/paper/2301.03421/) (2023)
- [FairFinGAN: Fairness-aware Synthetic Financial Data Generation](https://mlsanity.com/paper/2603.05327/) (2026)

## Abstract (arXiv)

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.