Learning Analytics of Online Students Performance in Mathematics Using Bayesian Network

Abstract

In recent years, there has been growing interest in online education, including e-learning, Massive Open Online Courses (MOOCs), Intelligent Tutoring System (ITS) and university-level distance learning. These online environments generate vast amounts of data, such as activity logs, course interaction data and assessment results, making learning analytics essential to predicting student performance. This study examines the efficacy of the probability-based model, Bayesian Networks (BNs), in predicting academic performance using learning analytics data collected through the Learning Management Systems (LMS). It focuses on how BN is capable of predicting student performance in an online learning environment by modeling complex relationships among various learning analytics factors that contribute to academic success. Using LMS data from Universiti Sains Malaysia's distance learning Mathematics course, the study incorporates key learning analytics variables such as engagement metrics, resource utilization, self-directed learning activities and assessment, and academic performance to develop a BN-based predictive model. BN model revealed that low engagement significantly hinders academic success, demonstrating its potential for early intervention and educational improvement. The model performance was measured using classification metrics such as accuracy, precision, recall, and F1-score. The developed model shows overall good performance, marked by strong precision and balanced recall in predicting the target classes with some variability. The results revealed that BN effectively captured dependencies among key learning analytics variables, providing actionable insights for designing personalized interventions in online education.