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 di erent 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 di erent policy implications and resource allocation recommendations.
