Predicting Students’ Algebra I Performance using Reinforcement Learning with Multi-Group Fairness

Abstract

Numerous studies have successfully adopted learning analytics techniques such as machine learning (ML) to address educational issues. However, limited research has addressed the problem of algorithmic bias in ML. In the few attempts to develop strategies to concretely mitigate algorithmic bias in education, the focus has been on debiasing ML models with single group membership. This study aimed to propose an algorithmic strategy to mitigate bias in a multi-group context. The results showed that our proposed model could effectively reduce algorithmic bias in a multi-group setting while retaining competitive accuracy. The findings implied that there could be a paradigm shift from focusing on debiasing a single group to multiple groups in educational attempts on ML.