Strategic Analytics for Predicting Students’ Academic Performance Using Cluster Analysis and Bayesian Networks

Abstract

The evolution of educational systems, marked by an increasing number of institutions, has prompted the integration of advanced data mining techniques to address the limitations of traditional pedagogical models. Predicting students’ academic performance, derived from large-scale educational data, has emerged as a critical application within educational data mining (EDM), a multidisciplinary field combining education and computational science. As educational institutions seek to enhance student outcomes and reduce the risk of failure, the ability to anticipate academic performance has gained considerable attention. A novel methodology, employing cluster analysis in combination with Bayesian networks, was introduced to predict student performance and classify academic quality. Students were first categorized into two distinct clusters, followed by the use of Bayesian networks to model and predict academic performance within each cluster. The proposed framework was evaluated against existing approaches using several standard performance metrics, demonstrating its superior accuracy and robustness. This method not only enhances predictive capabilities but also provides a valuable tool for early intervention in educational settings. The results underscore the potential of integrating machine learning techniques with educational data to foster more effective and personalized learning environments.