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 perfo... More >