A Multiobjective, Stable, and Fair Feature Selection and Ensemble Learning Framework for Explainable Student Academic Performance Prediction

Early intervention and data‐driven educational decision‐making depend on accurate student academic performance prediction; unfortunately, current methods frequently have high dimensionality, poor interpretability, and inadequate resilience. To obtain precise, understandable, stable, and equitable academic performance prediction, this study suggests an improved framework that combines a multiobjective Rüppell’s fox optimizer (RFO) with a gradient boosting classifier (GBC). An explicit trade‐off between predicted accuracy and interpretability is made possible by the formulation of the feature selection issue as a multiobjective optimization assignment that simultaneously minimizes classification error and feature subset size. The proposed RFO‐based method maintains good predictive performance while reducing the feature space to informative features, dependent on the chosen trade‐off parameter, using the students’ academic performance dataset. The most effective RFO–GBC design outperforms traditional classifiers with an overall classification accuracy of 91.46% and a weighted F1‐score of 91.30%. The efficacy of the proposed multiobjective formulation is demonstrated by a Pareto analysis, which shows that competitive accuracy (90.87%) can be achieved using a reduced feature subset. A stability analysis verifies the resilience of feature selection by several separate runs. The most significant predictors are completed credits and prior academic accomplishment, according to SHAP‐based explainability, which further improves the interpretability of the model. Furthermore, little predictive bias across sensitive attributes is indicated by a fairness evaluation based on equal opportunity and demographic parity, which supports ethical deployment in educational situations. Overall, the proposed framework offers a robust, interpretable, and fairness‐aware solution for student academic performance prediction, contributing to trustworthy educational data mining.