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Evaluating Mitigates of Primary School Dropout Risk Using Machine Learning in Narok West Sub-County, Kenya
Sylvia Cherop1
Emma Anyika2
James Obuhuma3
1 2Department of Computing and Mathematics, Co-operative University of Kenya, Kenya. 3Department of Mathematical Sciences, cooperative University, Kenya.
Published Online: September-December 2025
Pages: 94-99
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403018
References
1. Fernandes, M., Moreira, C., & Santos, J. (2021). Explainable dropout prediction using SHAP and XGBoost in higher education. Journal of Educational Data Science, 18(4), 321-340.
2. Kipuri, N., & Ridgewell, A. (2022). Education and Socioeconomic Challenges in Kenya. Oxford University Press.
3. Ministry of Education. (2021). Kenya Education Sector Report 2021. Nairobi, Kenya.
4. Mwangi, P., Ndungu, T., & Otieno, J. (2022). Data-driven approaches to school dropout prevention in Kenya: A policy review. African Journal of Educational Research, 9(2), 112-128.
5. Odhiambo, G., Wanjiku, J., & Njenga, P. (2021). The impact of cultural practices on education in Kenya.
6. African Journal of Education Studies, 12(3), 45-67.
7. Santos, A., & Moura, F. (2021). Random Forest for dropout prediction: A case study in secondary education.
8. Machine Learning in Education, 27(3), 198-210.
9. UNESCO. (2021). The role of predictive analytics in education policy-making: A global review. United Nations Educational, Scientific and Cultural Organization.
10. UNICEF. (2022). School dropout trends and policy interventions in sub-Saharan Africa. United Nations Children's Fund.
11. Wang, L., Zhang, H., & Li, T. (2022). Predicting student dropout using Gradient Boosting Machines: A case study in China. IEEE Transactions on Artificial Intelligence, 9(1), 23-37.
12. World Bank. (2020). Addressing school dropout through data-driven approaches. Washington, DC: World Bank Publications.
13. Zhang, Y., Lin, P., & Liu, D. (2020). Deep learning in dropout analysis: Challenges and insights. Journal of AI in Education, 15(2), 101-120.
14. Zhou, M., Wang, F., & Liu, X. (2022). The role of machine learning in student retention: A systematic review.
15. Educational Data Science Journal, 10(1), 78-95.
16. Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.
17. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
2. Kipuri, N., & Ridgewell, A. (2022). Education and Socioeconomic Challenges in Kenya. Oxford University Press.
3. Ministry of Education. (2021). Kenya Education Sector Report 2021. Nairobi, Kenya.
4. Mwangi, P., Ndungu, T., & Otieno, J. (2022). Data-driven approaches to school dropout prevention in Kenya: A policy review. African Journal of Educational Research, 9(2), 112-128.
5. Odhiambo, G., Wanjiku, J., & Njenga, P. (2021). The impact of cultural practices on education in Kenya.
6. African Journal of Education Studies, 12(3), 45-67.
7. Santos, A., & Moura, F. (2021). Random Forest for dropout prediction: A case study in secondary education.
8. Machine Learning in Education, 27(3), 198-210.
9. UNESCO. (2021). The role of predictive analytics in education policy-making: A global review. United Nations Educational, Scientific and Cultural Organization.
10. UNICEF. (2022). School dropout trends and policy interventions in sub-Saharan Africa. United Nations Children's Fund.
11. Wang, L., Zhang, H., & Li, T. (2022). Predicting student dropout using Gradient Boosting Machines: A case study in China. IEEE Transactions on Artificial Intelligence, 9(1), 23-37.
12. World Bank. (2020). Addressing school dropout through data-driven approaches. Washington, DC: World Bank Publications.
13. Zhang, Y., Lin, P., & Liu, D. (2020). Deep learning in dropout analysis: Challenges and insights. Journal of AI in Education, 15(2), 101-120.
14. Zhou, M., Wang, F., & Liu, X. (2022). The role of machine learning in student retention: A systematic review.
15. Educational Data Science Journal, 10(1), 78-95.
16. Becker, G. S. (1964). Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. University of Chicago Press.
17. Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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