A Proposed Hybrid Deep Learning and Ensemble Learning Model for Predicting Student Dropout in Education

Abstract

Student dropout remains a major challenge for higher education institutions due to its academic, social, and economic consequences. Early identification of students at risk of dropping out is essential for implementing timely interventions; however, existing prediction approaches often rely on standalone machine learning models that inadequately capture the complex, nonlinear relationships within heterogeneous educational data and exhibit reduced performance under class imbalance. To address these limitations, this study proposes a hybrid prediction framework that integrates deep neural representation learning with a stacked ensemble architecture to improve the accuracy and robustness of student dropout prediction. The proposed framework is evaluated on three benchmark educational datasets. A deep neural network is employed to learn latent feature representations from preprocessed student data, and the resulting embeddings are used to train heterogeneous machine learning classifiers, including XGBoost, Decision Tree, AdaBoost, Extra Trees, and K-Nearest Neighbors. Their probabilistic predictions are subsequently combined through a logistic regression meta-learner to generate the final prediction. To investigate the impact of class imbalance, both class weighting and the Synthetic Minority Over-sampling Technique (SMOTE) are incorporated and systematically evaluated. Model performance is assessed using accuracy, balanced accuracy, precision, recall, F1-score, and ROC-AUC, while permutation feature importance is employed to examine the influence of the original input variables on the overall prediction pipeline, and McNemar's test is used to assess the statistical significance of performance differences. Experimental results demonstrate that the proposed framework consistently outperforms standalone machine learning models, a standalone deep neural network, and conventional stacking approaches across all three datasets. These findings demonstrate that combining deep representation learning with stacked ensemble learning provides a robust and effective framework for early student dropout prediction, supporting data-driven educational interventions and informed decision-making in higher education.