Predicting Student Exam Performance Using Explainable Regression Models: A Learning Analytics Study

Abstract

Identifying which behavioral and demographic factors most strongly predict student academic performance is a long-standing goal of learning analytics. We apply linear regression, ridge regression, random forest, and gradient boosting to a 1,000-student behavioral and demographic dataset. Five-fold cross-validated R² shows that linear and ridge regression (R²=0.656) explain more variance than the ensemble methods (R²≈0.57), indicating a primarily additive structure in the data. To test this additive structure for residual non-linear or interaction effects and to obtain individual-level explanations, we apply SHapley Additive exPlanations (SHAP) to the random forest model via TreeExplainer, and cross-validate the resulting feature ranking against the standardized coefficients of the top-performing linear model. SHAP analysis identifies study time as the dominant predictor, with a mean |SHAP| of 4.93 points (approximately 4.1 exam-score points per additional study hour), followed by prior academic performance (mean |SHAP|=3.44) and attendance (mean |SHAP|=2.38). Internet access and part-time employment emerge as statistically meaningful binary factors (mean score gap: +5.0 and −3.3 points respectively), consistent with literature on the digital divide and academic workload. Parental education, gender, and extracurricular participation contribute negligible SHAP values (mean |SHAP| ≤0.16), a pattern that also holds for the corresponding linear coefficients. These results suggest that actionable behavioral variables – particularly study time and attendance – account for the bulk of explainable variance in examination outcomes, while fixed demographic characteristics contribute little incremental predictive power.