A predictive model for estimating students’ final cumulative GPA at graduation a case study of Makerere University

Abstract

Accurate prediction of students’ final Cumulative Grade Point Average (CGPA) at graduation is crucial for identifying at-risk students and improving academic outcomes. This study focuses on developing a predictive model for estimating CGPA/Degree Class using machine learning techniques, leveraging a dataset of over 2500 records collected from Makerere University graduates. The dataset encompasses academic performance, demographic details, and socioeconomic factors, mapped to official university records to ensure accuracy and credibility. Multiple machine learning models, including Logistic Regression, Gradient Boosting Classifier, and Random Forest Classifier, Dummy Classifier, Extra Trees Classifier, Neural Network MLPClassifier and Decision Tree Classifier were evaluated across three training-test splits (80/20, 75/25, and 70/30). Gradient Boosting Classifier emerged as the best-performing model, achieving consistent accuracy of approximately 84.7% and demonstrating robustness and interpretability. Feature importance analysis highlighted prior academic performance, students behaviour, and Alevel Year as the most significant predictors of CGPA, aligning with global findings and offering localized insights specific to Makerere University. Despite its contributions, the study faced challenges such as dataset size constraints and limited exploration of interdisciplinary variations. Nonetheless, the results underscore the potential of machine learning in academic performance prediction, providing actionable insights for educational institutions. The findings emphasize the importance of early intervention, targeted support, and data-driven decision-making in improving graduation rates. This research advances predictive analytics in education, offering a scalable framework for institutions. Future work should expand dataset size and include discipline-specific predictors for deeper analysis.