Predictive Analytics for Student Retention and Success Using AI/ML
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V4I4P114Keywords:
Predictive Analytics, Student Retention, Academic Success, Artificial Intelligence, Machine Learning, Educational Data Mining, Learning Analytics, Early Warning Systems, Higher EducationAbstract
Student retention and academic success are critical challenges faced by higher education institutions, particularly in the context of diverse student populations, large-scale enrollment, and digital learning environments. Traditional retention strategies often rely on retrospective analysis and limited indicators, restricting their ability to provide timely and proactive support. This study investigates the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques to develop predictive analytics models for early identification of at-risk students and enhancement of academic success outcomes. The proposed approach integrates academic records, behavioral engagement data, and demographic attributes collected from institutional systems and learning management platforms. A range of supervised machine learning and deep learning models, including ensemble methods and neural networks, are employed to predict student retention risk and performance. Feature engineering and explainable AI techniques are incorporated to improve model interpretability and support actionable decision-making for educators and administrators. Experimental evaluation using real-world higher education datasets demonstrates that AI/ML-based models achieve superior predictive performance compared to traditional statistical approaches, with improved accuracy, recall, and early-risk detection capability. The results highlight the potential of predictive analytics to enable proactive interventions, personalized academic support, and data-driven institutional strategies. The study also addresses ethical considerations related to data privacy, fairness, and responsible AI deployment in educational contexts. Overall, this research underscores the transformative role of AI-driven predictive analytics in fostering sustainable student retention and academic success in modern higher education systems
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