Educational Performance Prediction with Random Forest and Innovative Optimizers: A Data Mining Approach

Abstract

In the ever-evolving landscape of education, institutions grapple with the intricate task of evaluating individual capabilities and forecasting student performance. Providing timely guidance becomes pivotal, steering students toward specific areas for focused academic enhancement. Within the educational domain, the utilization of data mining emerges as a powerful tool, revealing latent patterns within vast datasets. This study adopts the Random Forest classifier (RFC) for predicting student performance, bolstered by the integration of two innovative optimizers—Victoria Amazonia Optimization (VAO) and Phasor Particle Swarm Optimizer (PPSO). A notable contribution of this research lies in the introduction of these novel optimizers to augment the model's accuracy, elevating the precision of predictions. Robust evaluation metrics, including Accuracy, Precision, Recall, and F1-score, meticulously gauge the model's effectiveness in this context. Remarkably, the results underscore the supremacy of RFC+VAO, showcasing exceptional values for Accuracy (0.934), Precision (0.940), Recall (0.930), and F1-score (0.930). This substantiates the significant contribution of integrating VAO into the Random Forest framework, promising substantial advancements in predictive analytics for educational institutions. The findings not only accentuate the efficacy of the proposed methodology but also herald a new era of precision and reliability in predicting student performance, thereby enriching the landscape of educational data analytics.