Student Dropout Prediction

Student dropout poses a significant challenge to educational institutions globally, necessitating proactive identification and intervention strategies.While existing research demonstrates strong predictive capabilities with machine learning approaches, critical gaps persist in evaluating intervention effectiveness, generating explainable recommendations, and addressing practical deployment considerations.This paper presents a comprehensive multi-modal framework that integrates academic performance data, behavioral indicators, financial status, and counseling records to predict dropout risk while providing actionable intervention recommendations.Our approach addresses key limitations in current literature through an explainability-to-action pipeline, systematic fairness analysis, and controlled intervention evaluation.Experimental validation using data from multiple educational institutions demonstrates 91.2% AUC-ROC performance with significant dropout reduction (28.5% relative improvement) in intervention groups compared to control conditions.Fairness analysis reveals minimal bias across demographic groups with implemented mitigation strategies, while educator usability evaluation shows 84% acceptance rates for system recommendations.The framework provides interpretable risk assessments with specific intervention priorities, demonstrating practical feasibility for deployment in resource-constrained educational environments.