Title: Student Dropout Prediction


Authors: Harshit Nagar, Kanak Chaurasia, Riya Agrawal, Vandana Kate


Published in: Volume 3 Issue 1 Jan June 2026, Page No. 109-115


DOI: 10.63844/IJAITR.v3.i1.2026.109-115 cite


Keywords: dropout prediction, educational data mining, explainable artificial intelligence, intervention evaluation, multi-modal learning analytics, fairness-aware machine learning


Abstract: 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.


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