Developing a Bayesian Network Model to Predict Students' Performance Based on the Analysis of their Higher Education Trajectory

The Admission Point Score (APS) metric, commonly used to admit prospective students into academic programmes, may appear effective in predicting 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. This research therefore focuses on adopting a probabilistic-graphical approach as a viable alternative to the APS metric for determining student success trajectories in higher education. 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.