PREDICTIVE ANALYTICS FOR STUDENT SUCCESS: AI-DRIVEN EARLY WARNING SYSTEMS AND INTERVENTION STRATEGIES FOR EDUCATIONAL RISK MANAGEMENT
Keywords:
Predictive analytics, Student success, Early warning systems, Educational data mining, Machine learning, Dropout prediction, Academic risk identification, Intervention strategiesAbstract
Predictive analytics has emerged as a transformative approach in educational technology, leveraging artificial intelligence and machine learning algorithms to identify at-risk students, predict academic outcomes, and recommend targeted interventions before failure occurs. This comprehensive review examines the current state of predictive analytics applications in education, analyzing methodologies, effectiveness, and implementation challenges across diverse educational contexts. Through systematic analysis of literature from 2019 to 2025, this study explores the technological foundations of early warning systems, including data mining techniques, feature engineering approaches, and predictive modeling frameworks. The review synthesizes empirical evidence from over studies demonstrating the effectiveness of predictive analytics in reducing dropout rates, improving retention, and enhancing overall student success outcomes. Key findings indicate that machine learning models can achieve prediction accuracies of 85-95% for identifying at-risk students, with ensemble methods and deep learning approaches showing superior performance compared to traditional statistical methods. Random forest and gradient boosting algorithms demonstrate particular effectiveness, achieving AUC scores of 0.92-0.96 in dropout prediction tasks. However, significant challenges persist in areas including data quality and integration, model interpretability, ethical considerations surrounding algorithmic decision-making, and the translation of predictions into effective interventions. The paper identifies emerging trends such as real-time analytics platforms, multimodal data integration, explainable AI frameworks, and automated intervention recommendation systems. Future research directions include the development of causal inference methods for intervention effectiveness, federated learning approaches for multi-institutional collaboration, and ethical frameworks for responsible deployment of predictive systems in educational contexts. This review contributes to understanding how AI-powered predictive analytics can transform educational support systems while highlighting critical considerations for implementation, scalability, and ethical use in diverse learning environments.References
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