Machine Learning and Deep Learning for Dropout Prediction in Higher Education: A Review

Beatriz Duro, Anabela Gomes, Fernanda Brito Correia, Ana Rosa Borges, Jorge Bernardino

Student dropout in Higher Education remains a persistent challenge with significant academic, social and economic consequences. Predictive analytics using traditional Machine Learning and Deep Learning have been increasingly explored to support early identification of students at risk. This article presents a structured literature review of studies published between 2018 and 2025 that apply these techniques to predict dropout in Higher Education. Unlike previous reviews, we pay particular attention to model interpretability, practical deployment and ethical considerations when analysing data types, preprocessing strategies and modelling approaches. Results show that transparent traditional models, including Decision Trees, Logistic Regression, and ensemble methods such as Random Forest and Gradient Boosting remain dominant because they perform strongly on structured data and are easier to explain. Deep Learning approaches, although less prevalent, show promise for sequential and behavioural data but face challenges in data availability, explainability, and implementation complexity. Despite frequently high reported performance, most studies rely on single-institution datasets, limiting generalisability, and only a minority address fairness, bias, or real-world integration. This analysis concludes that we must transition from accuracy-focused evaluations to transparent, accountable and actionable predictive systems that facilitate data-driven and inclusive decision-making in Higher Education.