  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.
