RXK-VEM: A Novel Probabilistically Calibrated Hybrid Ensemble Model for Predicting Student Dropout in Higher Education

A novel hybrid ensemble model combining Random Forest, XGBoost, and K-Nearest Neighbors with probabilistic fusion and meta-level calibration. RXK-VEM outperforms traditional classifiers in F1 score, AUC-ROC, and MCC, especially improving recall for underrepresented classes in imbalanced educational datasets.