Factors, Prediction, Explainability, and Simulating University Dropout Through Machine Learning: A Systematic Review, 2012–2024

Abstract

College dropout represents a significant challenge for universities, and despite advances in machine learning technologies, predicting dropout remains a complex task. This literature review focuses on investigating the factors that influence college dropout, examining the models used to predict it, and highlighting the most significant advances in explainability and simulation over the period 2012 to 2024 using the PRISMA methodology. They identified 520 factors in five categories (demographic, socioeconomic, institutional, personal, and academic), with the most studied factors in each category being, respectively, gender, scholarships, infrastructure, student identification, and grades. They also identified 83 machine learning models, with the most studied being the decision tree, logistic regression, and random forest. In addition, eight explanatory models were identified, with SHAP and LIME being the most widely used. Finally, no simulation models related to university dropout were identified. This study groups factors related to university dropout into key models for prediction and analyzes the methods used to explain the causal factors that influence university student dropout.

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