Predicting Student Performance Through Innovative Machine Learning Techniques

Abstract

The digital transformation in education, driven by platforms such as massive open online courses and virtual learning environments, has significantly broadened access to education globally. This study explores how machine learning (ML) can enhance and predict learning outcomes by identifying and supporting at-risk students through predictive models. The goal is to improve academic achievement by analyzing student profiles and addressing multiple contributing factors. In this research, ML classification models, specifically support vector classification (SVC), were employed along with two optimizers: the Ebola optimization algorithm and the attack-leave optimizer. The aim was to enhance the model's performance and prediction accuracy. The results indicate that in the training phase, the support vector-based ebola optimization (SVEO) model achieved an accuracy of 0.900, demonstrating moderate performance compared to the SVC model, which achieved an accuracy of 0.887. The support vector-based attack-leave optimization (SVAL) model, however, outperformed the others with the highest accuracy of 0.936.