A HYBRID MACHINE LEARNING FRAMEWORK FOR STUDENT ACADEMIC PERFORMANCE PREDICTION

Abstract

Student academic performance prediction is a serious trial in academic data mining, where initial and correct predicting allows targeted exclamation strategies. This paper suggests a novel hybrid machine learning framework that combines ensemble methods XGBoost and Random Forest, deep learning and Provision Vector Machine (PVM) within a stacked meta-learning construction. dissimilar define motivated techniques, our framework is better only for forecast correctness and simplification. Trained and assessed on an assorted dataset of 4,872 students calm from five educational organizations across 2019–2023, surrounding 26 attributes covering educational records, communication metrics, socio-economic gauges, appointment data, and demographic features, the future model achieves an accuracy of 93.7%, F1-Score of 92.1%, and AUC-ROC of 0.971, outstripping all six models through a minimum margin of 6.5% in correctness. Wide ablation studies authenticate apiece component’s influence to the complete implementation gain.