Predicting Students' Academic Performance Through Machine Learning Classifiers: A Study Employing the Naive Bayes Classifier (NBC)

Abstract

Modern universities must strategically analyze and manage student performance, utilizing knowledge discovery and data mining to extract valuable insights and enhance efficiency. Educational Data Mining (EDM) is a theory-oriented approach in academic settings that integrates computational methods to improve academic performance and faculty management. Machine learning algorithms are essential for knowledge discovery, enabling accurate performance prediction and early student identification, with classification being a widely applied method in predicting student performance based on various traits. Utilizing the Naive Bayes classifier (NBC) model, this research predicts student performance by harnessing the robust capabilities inherent in this classification tool. To bolster both efficiency and accuracy, the model integrates two optimization algorithms, namely Jellyfish Search Optimizer (JSO) and Artificial Rabbits Optimization (ARO). This underscores the research's commitment to employing cutting-edge machine learning and algorithms inspired by nature to achieve heightened precision in predicting student performance through the refinement of decision-making and prediction quality. To classify and predict G1 and G3 grades and evaluate students' performance in this study, a comprehensive analysis of the information pertaining to 395 students has been conducted. The results indicate that in predicting G1, the NBAR model, with an F1_Score of 0.882, performed almost 1.03% better than the NBJS model, which had an F1_Score of 0.873. In G3 prediction, the NBAR model outperformed the NBJS model with F1_Score values of 0.893 and 0.884, respectively.