Document
Metadata
Authors
Mukhtar Abdi Hassan, Abdisalam Hassan Muse, Saralees Nadarajah, Yahye Hassan Muse
Title
An Empirical Comparison of Supervised Machine Learning Models in Predicting Mathematics Performance in Somaliland
Journal
Springer Proceedings in Mathematics and Statistics
Year of Publication
2025
Abstract
Declining mathematics performance among primary school students in Somaliland, as evidenced by the increasing failure rate from 51.9% in 2020 to 65.58% in 2023, has prompted the need to investigate influencing factors and potential predictive models. This study leverages data from the 2022/2023 Somaliland National Examinations to identify and analyze these factors. Objective: The primary aim of this study was to apply and compare the effectiveness of various supervised machine learning models in predicting mathematics performance among primary school students and to identify the factors contributing to performance disparities. Methods: Data were drawn from the 2022/2023 Somaliland National Examination database, covering 20,950 students. Six supervised machine-learning models—Logistic Regression, Decision Tree, Random Forest, Naive Bayes, Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)—were applied to predict student performance. Performance metrics, such as accuracy, sensitivity, specificity, F1-score, and AUC, were used to evaluate the models. Results: Significant regional and demographic differences were observed between the groups. Regions such as Awdal and Maroodi Jeeh showed high failure rates, whereas Sheekh and Sanaag regions demonstrated higher success rates. Males (67.17%) failed more frequently than females (63.52%), and urban schools (67.64%) showed poorer performance than rural schools (45.21%). The Naive Bayes model achieved the highest accuracy of 98.6%, followed by KNN at 80.3%. Other models, such as Random Forest and Logistic Regression, demonstrated moderate success, whereas SVM performed the least effectively. Conclusions: The findings indicate that regional, sex, and school-type disparities significantly influence mathematics performance. The Naive Bayes model was the most effective in predicting performance, and these insights can be used for targeted interventions to improve educational outcomes. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
DOI
10.1007/978-3-031-84151-4_16
Category
Engineering, Computing, and IT