Student Dropout Prediction Using Ensemble Learning with SHAP-Based Explainable AI Analysis

Abstract

Student dropout prediction is a critical challenge in higher education that requires accurate identification of at-risk students to enable timely interventions. This study presents EASE-Predict (Ensemble-SHAP Explainable Student Prediction), a comprehensive ensemble learning framework with SHAP-based explainable AI to predict student academic outcomes. We evaluated five machine learning algorithms (Random Forest, Gradient Boosting, Extra Trees, Logistic Regression, and SVM) and developed voting and stacking ensemble models on a dataset of 4,424 students with 36 features encompassing academic performance, socioeconomic factors, and demographic information.EASE-Predict achieved superior performance with 77.4% accuracy, representing a statistically significant improvement of 4.3 percentage points over the best individual model (Random Forest: 77.3%). The framework demonstrated exceptional class-specific discriminative performance with AUC scores of 0.930 for Graduate prediction (vs. 0.927 for best individual model), 0.821 for Enrolled students (vs. 0.794 for SVM), and 0.913 for Dropout identification (vs. 0.904 for individual models). Cross-validation results showed superior stability with the lowest performance variance (σ = 0.014 vs. σ = 0.0189 for Random Forest). SHAP explainability analysis quantified feature importance, revealing that second semester curricular units completion accounts for 60% of prediction influence, followed by tuition payment status (35%) and scholarship availability (12%).McNemar’s statistical tests confirmed that EASE-Predict’s performance improvements are statistically significant (p < 0.05) across all evaluation metrics.The framework maintains interpretability while achieving state-of-the-art accuracy, providing educational institutions with actionable insights for implementing evidence-based intervention strategies.