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

Domaine:

education

Type de record:

paper
Créateur:
WanMarivate, VukosiSen
Éditeur:
arXiv
Hôte:avatar
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. In review

Visit

doi.orgarxiv.org

Tags

Computers and Society (cs.CY)Machine Learning (cs.LG)Applications (stat.AP)FOS: Computer and information sciencesFOS: Computer and information sciences

Licenses

Creative Commons Attribution Share Alike 4.0 Internationalhttps://creativecommons.org/licenses/by-sa/4.0/legalcode

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