This study investigates bias in college student success prediction models, focusing on racial disparities. Using the Education Longitudinal Study of 2002, the researchers evaluate the fairness of several machine learning models (Random Forest, SVM, Logistic Regression, Decision Tree) and the effectiveness of bias mitigation techniques (reweighting, Disparate Impact Remover, Exponentiated Gradient Reduction, Meta Fair Classifier). They find significant bias against Black and Hispanic students across various fairness metrics (statistical parity, equal opportunity, predictive equality, equalized odds) and limited success in mitigating this bias using existing techniques.
Publisher
International Conference on Educational Data Mining
Published On
Jan 01, 2023
Authors
Hadis Anahideh, Matthew P Ison, Anuja Tayal, Denisa Gándara
Tags
bias
college student success
racial disparities
machine learning
fairness metrics
bias mitigation
educational predictions
Related Publications
Explore these studies to deepen your understanding of the subject.
