Intelligent profiling beyond grades using a stacking ensemble framework for student success prediction

The study integrated machine learning (ML) framework for predicting students’ success in academics via 2-phased experiments with emotional intelligence and personality traits. In the initial experiment, unsupervised learning (K-Means clustering) was utilized to unearth hidden success levels from unlabeled data. This precedes training of standalone ML classifiers [Random Forest (RF), Support Vector Machine (SVM), and Extreme Gradient Boosting (XGBoost)] on clustered-derived labels. Phase 2 experiment; ensemble model was developed by integrating the standalone ML models via stacking and voting using Logistic Regression as the meter-learner. Models’ evaluation was done with Accuracy, F1-score, Recall, AUC-ROC, and Precision. The proposed ensemble model recorded optimal results for all the metrices, outperforming standalone models. Comparative analysis with previous studies validated the superior generalizability of the proposed model. Decision Tree was used to enhance interpretability, unveiling Conscientiousness, Stress Management, and Trait Empathy as dominant predictors. The study’s findings emphasized the relevance of integrating affective and personality factors in educational analytics as well as the need for interpretable, ensemble-based predictive tools for early risk detection to enhance decision-making in pursuance to students’ academic success rate.

Decision tree · Empathy · Ensemble forecasting · Ensemble learning · Generalizability theory · Gradient boosting · Personality · Random forest · Support vector machine · Emotional Intelligence and Performance · Intelligent Tutoring Systems and Adaptive Learning · Online Learning and Analytics

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