Predicting student success: a state-of-the-art systematic review of models and factors

Purpose This study aims to systematically review the methods, predictive factors and evaluation metrics used to forecast university students’ academic performance. By synthesizing recent literature, it addresses the lack of an integrated overview of state-of-the-art approaches and key determinants, and provides evidence-based insights to inform personalized teaching strategies and early intervention practices in higher education. Design/methodology/approach A systematic literature review (SLR) was conducted in accordance with the PRISMA framework. Articles published between 2015 and 2024 were retrieved from the Web of Science database using predefined keywords and Boolean search strategies. Following a structured screening process based on explicit inclusion and exclusion criteria, 31 studies were selected for analysis. These studies were systematically examined to synthesise predictive methods, key influencing factors and commonly used evaluation metrics in forecasting university students’ academic performance. Findings Academic performance is influenced by personal factors (e.g. entry scores, prior Grade Point Average [GPA], gender, age, emotional responses, self-esteem) and external factors (e.g. family income, parental education, accommodation, learning environment). Main prediction approaches include statistical regression models, machine learning algorithms (e.g. random forest, support vector machine (SVM), gradient boosting) and deep learning techniques. Machine learning shows strong performance with complex, non-linear data, while deep learning achieves high accuracy but requires large datasets. Common evaluation metrics include accuracy, precision, recall, F1 score and mean squared error (MSE). Originality/value This study provides a systematic and integrative synthesis of predictive methods, influencing factors and evaluation metrics related to university students’ academic performance. By consolidating evidence that has previously been scattered across methodological and thematic strands, the review offers a structured reference for researchers and actionable insights for educators and policymakers to support early identification of at-risk students and inform data-driven educational decision-making.