Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning
This work addresses resource allocation challenges for school improvement in African contexts, but it is incremental as it applies existing methods to new data.
The study applied machine learning to education data from South Africa and Sierra Leone to identify determinants of school performance, finding that key factors varied between the countries, leading to different policy recommendations.
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 and school-level features. We deploy interpretable metrics from machine learning approaches such as SHAP values on tree models and odds ratios of LR 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.