Using Data Mining to Predict Secondary School Student Performance for Zambia
Mainess Kandah Namuchile1, Christopher Mulwanda2
1Mainess Kandah Namuchile, Department of Computer Science, School of Engineering and Technology, Mulungushi University, Zambia.
2Dr. Christopher Mulwanda, Researcher, Centre for Environmental Justice, Plot 37741, Pitta Road, off Twin-Palm Road, Ibex Hill, Lusaka, Zambia.
Manuscript received on 29 December 2025 | First Revised Manuscript received on 18 February 2026 | Second Revised Manuscript received on 03 March 2026 | Manuscript Accepted on 15 March 2026 | Manuscript published on 30 March 2026 | PP: 7-13 | Volume-14 Issue-6, March 2026 | Retrieval Number: 100.1/ijrte.E832914050126 | DOI: 10.35940/ijrte.E8329.14060326
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Predicting student performance remains a challenge in many education systems, especially in developing countries like Zambia, where robust predictive tools are scarce. This study shows how data mining methods can be utilised to improve the accuracy of performance prediction by leveraging mock examination results to meet the needs of school management. For this study, a dataset containing 1,170 instances and 17 attributes was constructed and analysed using four classification algorithms (J48, PART, BayesNet, and Random Forest). The findings indicate that although each classifier produced results with high accuracy above 99%, Random Forest performed best, delivering perfect predictions with 100% accuracy. These results emphasise the importance of data mining in generating reliable forecasts of student performance, enabling early detection of at-risk learners and timely interventions by school managers, teachers, and parents. The study recommends adopting Random Forest as the most suitable classifier for predicting student performance. By incorporating predictive analytics into educational management, schools can strengthen decision-making, refine teaching approaches, and ultimately improve learning quality.
Keywords: Data Mining: Performance Prediction: Classification Algorithms: Random Forest: Zambia Secondary School.
Scope of the Article: Computer Science and Engineering