Predicting Student Attrition in Kenyan Universities: A Comparative Analysis of Machine Learning Algorithms

Predicting Student Attrition in Kenyan Universities: A Comparative Analysis of Machine Learning Algorithms

Authors

  • Lilian Nyawira Technical University of Mombasa, Kenya
  • Obadiah Musau Technical University of Mombasa, Kenya
  • Aggrey Adem Technical University of Mombasa, Kenya
  • Eric Jobunga Technical University of Mombasa, Kenya

DOI:

https://doi.org/10.62049/jkncu.v5i2.313

Keywords:

Student Attrition, Machine Learning, Classification Algorithms, Logistic Regression, Naïve Bayes, Decision Trees

Abstract

One of the primary goals of higher education institutions is to provide high-quality education and ensure a high completion rate. Reducing student attrition is one strategy for attaining high-quality education. Identifying students who are susceptible to dropping out and the variables that lead to dropouts are essential to achieving this. The purpose of this research was to ascertain how machine learning models might be used to forecast student attrition in Kenyan universities. Based on a number of classification criteria, such as F1 score, precision and accuracy, the study assessed and contrasted the performance of numerous algorithms, including Decision Trees, Random Forest, Naive Bayes, and Logistic Regression. The analysis demonstrated how well Logistic Regression worked, outperforming the other models and consistently striking a balance between precision and recall. Decision Trees and Random Forest, despite showing improvements through hyperparameter tuning, still struggled to identify students at risk of attrition. Naive Bayes, while relatively balanced, did not match the performance of Logistic Regression. The study provided a comprehensive overview of each model's strengths and limitations and suggests future work to further optimize the models for better predictive performance.

Author Biographies

Lilian Nyawira, Technical University of Mombasa, Kenya

Department of Mathematics and Physics

Obadiah Musau, Technical University of Mombasa, Kenya

Institute of Computing and Informatics

Aggrey Adem, Technical University of Mombasa, Kenya

Institute of Computing and Informatics

Eric Jobunga, Technical University of Mombasa, Kenya

Department of Mathematics and Physics

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Published

2025-07-30

How to Cite

Nyawira, L., Musau, O., Adem, A., & Jobunga, E. (2025). Predicting Student Attrition in Kenyan Universities: A Comparative Analysis of Machine Learning Algorithms. Journal of the Kenya National Commission for UNESCO, 5(2). https://doi.org/10.62049/jkncu.v5i2.313

Issue

Section

Education

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