What Are the Best Predictors of Learning Outcomes in Sub-Saharan Africa?

Abstract

This study attempts to discover the best predictors of mathematics and language learning outcomes across Kenya, Mozambique, Nigeria, Uganda, and Tanzania by analysing World Bank SDI data and using machine learning methods for variable selection purposes. Firstly, I use the SDI data to show the current fragilities in the quality of education service delivery, while also highlighting deficiencies in student learning outcomes. Then, I use CV Lasso, Adaptive Lasso, and Elastic Net regularisation methods to help discover the best predictors of learning outcomes. While the results from the regularisation methods show that private schools, teacher subject knowledge, and teacher pedagogical skills are good predictors of learning outcomes in a sample combining observations from Kenya, Mozambique, Nigeria, Uganda, and Tanzania, the results fail to infer causality by not distinguishing if unobservable factors are driving the results. To quantify the relationship of key predictors, and for statistical significance testing purposes, I then conduct subsequent OLS analysis. Despite not expecting the true partial derivative effects to be identical to the OLS coefficients presented in this study, this study highlights deficiencies in education service delivery and applies methods which help select key predictors of learning outcomes across the sampled schools in the SDI data.