Volume 11 • Issue 2 • PP: 20–34 • 2026
A Hybrid Al-Biruni Earth Radius–Stochastic Fractal Search Optimized XGBoost Model for Accurate Student Performance Prediction
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The ability to accurately predict student academic performance is now an established pillar of contemporary educational data mining, driven by the desire for data-driven interventions and personalized learning. This paper presents a mixed-methodology optimization and machine learning model that combines the Al-Biruni Earth Radius-Stochastic Fractal Search (BER-SFS) algorithm with eXtreme Gradient Boosting (XGBoost) to improve predictive accuracy and model stability in forecasting student performance. The proposed BER-SFS + XGBoost model is an optimized system that systematically optimises the hyperparameter based on dual exploration-exploitation mechanism to reduce the Mean Squared Error (MSE) of the baseline XGBoost model of 0.0226 to 0.00029 and the Root Mean Squared Error (RMSE) of the baseline XGBoost model to 0.1504 to 0.00194 and the coefficient of determination R2 of 0.9019 to Comparison to the other metaheuristics, including theGrey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO) and Whale Optimization Algorithm (WOA) proved the superiority of the suggested hybrid model in all metrics of the evaluation. These findings support the potential to combine the dynamics of geometric exploration provided by BER with those of diffusion-based refinement offered by SFS to achieve high generalization and minimal bias. The implications extend beyond predictive analytics, demonstrating that the hybrid metaheuristic optimization approach can serve as a scalable, explainable method for adaptive educational systems, enabling early detection of at-risk learners and the development of data-driven academic support mechanisms.
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