TY  - JOUR
AU  - Liu, Ziyang
AU  - Zhou, Xiang
AU  - Liu, Yijun
PY  - 2025
DA  - 2025/08/06
TI  - Student Dropout Prediction Using Ensemble Learning with SHAP-Based Explainable AI Analysis
JO  - Journal of Social Systems and Policy Analysis
T2  - Journal of Social Systems and Policy Analysis
JF  - Journal of Social Systems and Policy Analysis
VL  - 2
IS  - 3
SP  - 111
EP  - 132
DO  - 10.62762/JSSPA.2025.321501
UR  - https://www.icck.org/article/abs/JSSPA.2025.321501
KW  - student dropout prediction
KW  - ensemble learning
KW  - explainable AI
KW  - SHAP analysis
KW  - educational data mining
KW  - machine learning
AB  - 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.
SN  - 3068-5540
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 

@article{Liu2025Student,
  author = {Ziyang Liu and Xiang Zhou and Yijun Liu},
  title = {Student Dropout Prediction Using Ensemble Learning with SHAP-Based Explainable AI Analysis},
  journal = {Journal of Social Systems and Policy Analysis},
  year = {2025},
  volume = {2},
  number = {3},
  pages = {111-132},
  doi = {10.62762/JSSPA.2025.321501},
  url = {https://www.icck.org/article/abs/JSSPA.2025.321501},
  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.},
  keywords = {student dropout prediction, ensemble learning, explainable AI, SHAP analysis, educational data mining, machine learning},
  issn = {3068-5540},
  publisher = {Institute of Central Computation and Knowledge}
}