Volume 11 • Issue 2 • PP: 35–54 • 2026
A Hybrid Al-Biruni Earth Radius–Random Forest Model for Accurate and Efficient Student Performance Classification
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The growing availability of educational data has prompted the use of machine learning methods to predict student academic performance and support data-driven decision-making in education. Nevertheless, such models for predicting performance rely heavily on proper data preprocessing, model selection, and optimal hyperparameter settings. This research proposes a hybrid predictive architecture that combines machine learning classifiers with bio-inspired metaheuristic optimization algorithms to improve classification efficiency in educational data mining. It is based on the xAPI-Edu.A dataset of 480 students’ demographic, academic, and behavioral characteristics is used to first analyze a set of baseline machine learning models, including Random Forest, XGBoost, Support Vector Machine, Multilayer Perceptron, K-Nearest Neighbors, and Gaussian Naive Bayes, using standard classification metrics. The initial experimental findings on the baseline layer show that the Random Forest classifier outperforms the other models before optimization, achieving accuracies of 0.8889 and 0.8814, and F-scores of 0.8889 and 0.8814, respectively, indicating strong generalization and equal discrimination among the classes. To further improve the predictive performance, the state-of-the-art metaheuristic algorithms, i.e., the Al-Biruni Earth Radius Optimizer (BER), the Gray Wolf Optimizer (GWO), the Particle Swarm Optimization (PSO), the Genetic Algorithms (GA) and the Whale Optimization Algorithms (WOA) are adopted to optimize the hyperparameters of the Random Forest. It has been experimentally demonstrated that every optimization approach provides a measurable performance increase, but the BER-optimized Random Forest consistently performs better. In particular, the BER-Random Forest model achieves an F-score of 0.9477 and an accuracy of 0.9439, both of which are much higher than the baseline configuration. Full statistical and visual analyses, such as kernel density estimation, Z-score heatmaps, and swarm plots, also support the strength, stability and superiority of the proposed BER-based optimization framework. Such findings demonstrate the effectiveness of metaheuristic-based hyperparameter optimization in educational predictive analytics and provide significant insights into the creation of intelligent, efficient, and data-driven systems of academic assistance.
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