Interpretable machine learning for academic performance prediction: A SHAP-based analysis of key influencing factors

This study employs machine learning approaches to predict the final exam scores of vocational undergraduate students and analyse critical factors influencing their academic performance. Using a multidimensional feature dataset, Ridge Regression was set as a baseline model, while four mainstream machine learning models - Random Forest, XGBoost, Support Vector Machine and Neural Network - were utilised for predictive modelling, with Random Forest achieving the best performance. SHapley Additive exPlanations (SHAP) was applied to interpret global and local feature contributions, indicating monthly exam scores, admission scores and self-study time as the most influential predictors, whereas demographic features were comparatively less significant. Furthermore, Partial Dependence Plots (PDP) and Kernel Density Estimation (KDE) analyses were conducted to explore feature interactions and differences between high- and low-achieving students, offering practical insights for vocational institutions to implement precise interventions focusing on key predictive factors.