Developing a Bayesian Network Model to Predict Students’ Performance Based on the Analysis of their Higher Education Trajectory - Ramaano, Thabo Victor (University of the Witwatersrand, Johannesburg, 2024)
The Admission Point Score (APS) metric, utilised as a response to admit prospective students for an academic course, may appear effective in determining student success. In reality, almost 50% of students admitted to a science programme in a higher education institution failed to meet all the requirements necessary to complete the programme during the period of 2008 and 2015. This had a direct impact on the overall graduation throughput. Thus, the focus of this research was geared towards the adoption of a probabilistic graphical approach to advocate its mechanism as a viable alternative to the APS metric when determining student success trajectories at a higher education level. The purpose of this approach was to provide higher education institutions with a system to monitor students’ academic performance en-route to graduation from a probabilistic and graphical point of view. This research employed a probability distribution distance metric to ascertain how close the learned models were to the true model for varying sample sizes. The significance of these results addressed the need for knowledge discovery of dependencies that existed between the students’ module results in a higher education trajectory that spans three years.
A dissertation submitted in fulfillment for the degree of Master of Science in Computer Science (Research), to the Faculty of Science, School of Computer Science and Applied Mathematics, at the University of the Witwatersrand, Johannesburg, 2024
Keywords: Bayesian network; Higher education trajectory; Hill-climbing structure learning algorithm; Kullback-Leibler divergence; Variable elimination inference algorithm; UCTD; SDG-4: Quality education