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T1  - What Are the Best Predictors of Learning Outcomes in Sub-Saharan Africa? 
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UR  - URN:NBN:fi:hulib-202110063845; http://hdl.handle.net/10138/334987 
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A1  - Savolainen, Dominic 
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PB  - Helsingin yliopisto 
Y1  - 2021 
LA  - eng 
AB  - 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 met... 
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KW  - learning outcomes; lasso; elastic net; regularisation; regularization; machine learning; classification; prediction; causality; education; teachers; schools; students; africa; Taloustieteen yleinen opintosuunta; General track; Allmänna studieinriktningen; Taloustieteen maisteriohjelma; Master's Programme in Economics; Magisterprogrammet i ekonomi 
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