Beyond Performance: Explaining and Ensuring Fairness in Student Academic Performance Prediction with Machine Learning

Beyond Performance: Explaining and Ensuring Fairness in Student Academic Performance Prediction with Machine Learning

This study addresses fairness in machine learning for student academic performance prediction using the UCI Student Performance dataset. We comparatively evaluate logistic regression, Random Forest, and XGBoost, integrating the Synthetic Minority Oversampling Technique (SMOTE) to address class imbal...

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Bibliographic Details
Main Authors: Kadir Kesgin, Salih Kiraz, Selahattin Kosunalp, Bozhana Stoycheva
Format: Article
Language:English
Published: MDPI AG 2025-07-01
Series:Applied Sciences
Subjects:
student performance prediction
logistic regression
explainable AI (XAI)
SHAP
fairness in education
adversarial debiasing
Online Access:https://www.mdpi.com/2076-3417/15/15/8409
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