Authors - Jorge Duque, Antonio Godinho, Jose Moreira, Firmino Silva Abstract - Student dropout in higher education remains a persistent academic, institutional and social challenge, requiring evidence-informed responses. This paper develops and evaluates an explainable machine learning artefact for early dropout-risk identification and for translating predictions into tiered institutional interventions. The study follows six phases of Design Science Research and uses the public Predict Students' Dropout and Academic Success benchmark, with 4,424 students and 37 variables. The pipeline integrates one-hot encoding, derived features, stratified validation, SMOTE applied only inside training folds, Random Forest, XGBoost and SVM as base learners, stacking with a logistic meta-learner and SHAP explanations. The final ensemble achieved 96.4% accuracy, 0.965 weighted precision, 0.964 weighted recall, 0.964 weighted F1-score and 0.99 weighted AUC. The contribution lies in combining performance, interpretability, governance and responsible human intervention.
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