Cross-Institution Student Performance Prediction Using an Explainable Stacking Ensemble Framework
This study presents an explainable machine learning framework for early prediction of student academic performance using a stacking-based ensemble approach. The proposed model integrates Random Forest (RF), XGBoost, and Support Vector Machine (SVM) as base learners, with Logistic Regression (LR) used as a meta-learner....