ARCHIVES
Case Study
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.
Related Articles
2025
Transforming Cyber-Physical Systems: Machine Learning for Secure and Efficient Solutions
2025
Exploring AI Techniques for Quantum Threat Detection and Prevention
2025
Maturity Models for Business Intelligence: An Overview
2025
INSPIRO: An AI Driven Institution Auditor
2025
Adaptive AI Framework for Anomaly Detection and DDoS Mitigation in Distributed Systems
2025
Predictive Modeling for College Admission Using Machine Learning and Statistical Methods
Share Article
Or copy link
https://www.indjcst.com/archives/evaluating-mitigates-of-primary-school-dropout-risk-using-machine-learning-in-narok-west-sub-county-kenya
*Instagram doesn't support direct link sharing from web. Copy the link and share it in your Instagram story or post.