Machine Learning Grade Prediction Using Students' Grades and Demographics

Authors

Mwayi Sonkhanani, Malawi University of Science and Technology, Malawi

Symon Chibaya, Malawi University of Science and Technology, Malawi

Clement Nyirenda, University of the Western Cape, South Africa

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/tbc

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Student repetition in secondary education imposes significant resource burdens, particularly in resource-constrained contexts. Addressing this challenge, this study introduces a unified machine learning framework that simultaneously predicts pass/fail outcomes and continuous grades - a departure from prior research that treats classification and regression as separate tasks. Six models were evaluated: Logistic Regression, Decision Tree, and Random Forest for classification, and Linear Regression, Decision Tree Regressor, and Random Forest Regressor for regression, with hyperparameters optimized via exhaustive grid search. Using academic and demographic data from 4,424 secondary school students, classification models achieved accuracies of up to 96%, while regression models attained an R² of 0.70, surpassing baseline approaches. These results confirm the feasibility of early, data-driven identification of at-risk students and highlight the value of integrating dual-task prediction for more comprehensive insights. By enabling timely, personalized interventions, the framework offers a practical pathway to reducing grade repetition and optimizing resource allocation.

Keywords

Machine learning, Student performance prediction, Hyperparameters, Demographics, Classification, Regression

Cite this paper

M. Sonkhanani, S. Chibaya and C. Nyirenda (2026) "Machine Learning Grade Prediction Using Students' Grades and Demographics", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/tbc

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