Predicting final CGPA using pre-admission data: proactive insights for academic excellence at Uganda Christian University

Abstract

Higher education institutions increasingly rely on data-driven approaches to improve student support, academic planning, and decision-making. However, the adoption of predictive analytics in Sub-Saharan African universities remains limited, despite the availability of admission records that could inform early academic guidance. This study developed and evaluated a machine learning model for predicting students’ final Cumulative Grade Point Average (CGPA) using pre-admission academic and demographic data from Uganda Christian University. The study employed a quantitative research design using historical student records extracted from the university’s Management Information System. The dataset included O-Level and A-Level academic performance indicators, demographic attributes, and programme-related variables. Several machine learning models were trained and evaluated, with the Random Forest Regressor selected as the best-performing model after hyperparameter optimisation. Model performance was assessed using Mean Absolute Error, Root Mean Squared Error, and the coefficient of determination. To support transparency and responsible use, SHAP-based interpretability, sensitivity analysis, and subgroup fairness evaluation were incorporated. The findings showed that final CGPA can be predicted from admission-time data with moderate but useful accuracy. Prior academic performance, particularly average O-Level grade, weighted A-Level performance, and UCE credits, emerged as the strongest predictors of final CGPA. Fairness analysis across gender, campus, and academic level indicated generally consistent model performance, although continued monitoring is necessary for underrepresented groups. The study further demonstrated how predicted CGPA bands and programme-fit simulations can support proactive academic advising, early identification of students requiring support, and evidence-informed programme guidance. The study concludes that interpretable and fair machine learning models can provide practical value in Ugandan higher education when used as decision-support tools rather than deterministic placement mechanisms. By using pre-admission data already available within institutional systems, universities can strengthen academic advising, improve student support, and promote more evidence-based planning.