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Available or adequate information to inform decision making for resourceallocation in support of school improvement is a critical issue globally. Inthis paper, we apply machine learning and education data mining techniques oneducation big data to identify determinants of high schools' performance in twoAfrican countries: South Africa and Sierra Leone. The research objective is tobuild predictors for school performance and extract the importance of differentcommunity and school-level features. We deploy interpretable metrics frommachine learning approaches such as SHAP values on tree models and odds ratiosof LR to extract interactions of factors that can support policy decisionmaking. Determinants of performance vary in these two countries, hencedifferent policy implications and resource allocation recommendations.

Investigating similarities and differences between South African and Sierra Leonean school outcomes using Machine Learning