[Dissertation] Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning

Members

Henry Wandera, MITC Big Data Science

Supervisor(s)

Dr. Vukosi Marivate

Abstract

Available or adequate information to inform decision making for resource allocation in support of school improvement is a critical issue globally. In this paper, we apply machine learning and education data mining techniques on education big data to identify determinants of high schools’ performance in two African countries: South Africa and Sierra Leone. The research objective is to build predictors for school performance and extract the importance of different community-level and school-level features. We deploy interpretable metrics from machine learning approaches such as SHAP values on tree models and Logistic Regression odds ratios to extract interactions of factors that can support policy decision making. Determinants of performance vary in these two countries, hence different policy implications and resource allocation recommendations.

Publications

• Wandera, H., Marivate, V. and Sengeh, M.D., 2019, November. Predicting National School Performance for Policy Making in South Africa. In 2019 6th International Conference on Soft Computing & Machine Intelligence (ISCMI) (pp. 23-28). IEEE. [Link]
• Wandera, H., Marivate, V. and Sengeh, D., 2020. Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning. arXiv preprint arXiv:2004.11369. [Link].