Predictive Modeling For Student Academic Performance: A Data Analytics Approach To Early Intervention

Abstract

The early identification of students at risk of academic underperformance is a critical challenge facing educational institutions globally. Traditional approaches to monitoring student progress often rely on reactive measures, intervening only after academic decline has become apparent. This paper presents a comprehensive finding-based review of predictive modeling approaches for student academic performance, synthesizing evidence from recent empirical studies and systematic reviews. The analysis demonstrates that machine learning and deep learning models, including ensemble methods such as Random Forest and hybrid neural network architectures, consistently achieve high predictive accuracy—with reported performance reaching 98.85% in classification tasks and R² values of 0.93 in regression-based predictions. Key predictors identified across studies include prior academic achievement, learning management system engagement metrics, attendance patterns, and demographic characteristics. The findings indicate that multidimensional data integration, combining academic, behavioral, and temporal features, significantly enhances predictive performance compared to single-source approaches. However, critical challenges persist regarding model interpretability, algorithmic bias, data privacy, and practical integration into institutional workflows. The paper proposes a framework for implementing predictive analytics within student management systems, emphasizing the need for context-sensitive, ethically-grounded, and intervention-driven applications that prioritize student support over purely technical optimization.