A Machine Learning approach for identifying at risk pupils and recommending support strategies: a case study of primary schools in Mukono District, Uganda

Abstract

Academic vulnerability and pupil dropout remain persistent challenges in Ugandan primary education, despite high enrollment rates. Current school support systems are often reactive, intervening only after academic failure has occurred. This study developed a predictive early warning system to proactively identify pupils at risk of academic failure in Mukono District, Uganda. A mixed-methods approach was used, analysing structured records of pupils from Primary 4–6 and conducting interviews with teachers and administrators. The study first identified key behavioural and socioeconomic predictors of academic risk through statistical analysis. Four machine learning models were then evaluated and compared to determine the most effective approach for predicting vulnerability. The analysis revealed that behavioural indicators, specifically disciplinary issues, incomplete homework, and poor attendance, were the strongest predictors of academic risk. Among the models tested, Logistic Regression proved most suitable, achieving a recall of 0.833 and ROC-AUC of 0.941 on unseen test data, while providing interpretable predictions crucial for educational settings. Based on these findings, a three-tiered intervention framework was developed, classifying pupils by risk level and linking specific risk factors to tailored support strategies. The study concludes that a simple, interpretable predictive model using routinely collected school data can effectively identify vulnerable pupils early. The proposed framework offers Ugandan primary schools a practical, proactive tool for targeted intervention, shifting support from crisis management to prevention. This research contributes a feasible, evidence-based approach to enhancing educational equity and retention in resource-constrained settings.