GWO-Optimized Ensemble Learning for Interpretable and Accurate Prediction of Student Academic Performance in Smart Learning Environments

Accurate and interpretable prediction of student academic performance is a cornerstone of data-driven educational support systems, enabling timely interventions, personalized learning pathways, and equitable resource allocation. While ensemble machine learning models such as Random Forest, Extra Trees, and CatBoost have shown promise in educational data mining, their predictive power and generalizability are often limited by suboptimal weighting schemes and sensitivity to hyperparameter configurations. To address this, we propose a Grey Wolf Optimizer (GWO)-guided ensemble framework that dynamically optimizes each base regressor’s contribution to minimize prediction error while preserving model transparency. Evaluated on a real-world student performance dataset, the proposed approach achieves a coefficient of determination (R2) of 0.93, significantly outperforming individual and conventional ensemble baselines. Furthermore, we integrate SHAP (SHapley Additive exPlanations) to provide educator-friendly interpretability, revealing that daily study hours, study effectiveness, lifestyle score, and screen time are the most influential predictors of exam outcomes. By bridging an optimized machine learning model with educational analytics, this work delivers a robust, transparent, and high-performing AI solution tailored for intelligent tutoring systems, early-warning platforms, and adaptive learning environments. The methodology exemplifies how nature-inspired optimization can enhance not only accuracy but also actionable insight for stakeholders in smart education ecosystems.