Prediction of Underperforming Students Using Boosting Ensemble Methods

This study compares boosting and ensemble methods for predicting at-risk students using three public datasets. Models like AdaBoost, CatBoost, and Random Forest are evaluated for early warning of underperformance. A practical dashboard concept is proposed for teachers and students. The work supports interpretable and actionable educational interventions.