Analysis of Academic Performance using Learning Management System (LMS) Data and Bayesian Network

Abstract

Learning Management Systems (LMS) have become integral tools in higher education, generating vast amounts of data that can be leveraged to analyze and enhance academic performance. Despite the abundance of this data, effectively harnessing it to understand complex relationships between learning activities and student outcomes remains a challenge. This paper explores the application of Bayesian Network (BN), a powerful technique in Educational Data Mining (EDM) to model and predict student outcomes using LMS data. BN provides a probabilistic framework to explore how various learning analytics variables influence academic success. Using LMS data from an online undergraduate Mathematics course, the model investigated the impact of student engagement, resource utilization, and participation on exam grades. The results show that consistent attendance (88%), active participation in lecturing sessions (85%), and involvement in online mathematical laboratory activities (62%), despite lower engagement in other areas such as assessments and gamification, are strongly associated with favourable final exam outcomes (62% achieving ‘Good’ or ‘Excellent’ grades). Numerical simulations were conducted to explore future student outcomes by manipulating key variables, demonstrating the potential of improved learning strategies such as full participation, improved prior knowledge and complete utilization of digital resources. This study highlights the utility of BN in analyzing LMS data to inform educational practices and ultimately enhance academic performance in higher education.