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Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning
Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2020.
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| Other Authors: | |
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| Format: | Thesis |
| Language: | English |
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University of Pretoria
2022
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| _version_ | 1867613450266476544 |
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| access_status_str | Open Access |
| author2 | Marivate, Vukosi |
| author_browse | Marivate, Vukosi |
| author_facet | Marivate, Vukosi |
| collection | Thesis |
| dc_rights_str_mv | © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. |
| description | Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2020. |
| format | Thesis |
| id | oai:repository.up.ac.za:2263/83192 |
| institution | University of Pretoria (South Africa) |
| language | English |
| last_indexed | 2026-06-10T12:36:20.438Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from UPSpace — University of Pretoria Institutional Repository |
| publishDate | 2022 |
| publishDateRange | 2022 |
| publishDateSort | 2022 |
| publisher | University of Pretoria |
| publisherStr | University of Pretoria |
| record_format | dspace |
| source_str | UPSpace — University of Pretoria Institutional Repository |
| spelling | oai:repository.up.ac.za:2263/83192 Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning Marivate, Vukosi Wandera, Henry UCTD Education Policy-making and Machine learning Mini Dissertation (MIT (Big Data Science))--University of Pretoria, 2020. 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. Computer Science MIT (Big Data Science) Unrestricted 2022-01-12T06:00:08Z 2022-01-12T06:00:08Z 2021/04/13 2020 Mini Dissertation * A2021 http://hdl.handle.net/2263/83192 en © 2021 University of Pretoria. All rights reserved. The copyright in this work vests in the University of Pretoria. No part of this work may be reproduced or transmitted in any form or by any means, without the prior written permission of the University of Pretoria. application/pdf University of Pretoria |
| spellingShingle | UCTD Education Policy-making and Machine learning Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title | Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title_full | Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title_fullStr | Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title_full_unstemmed | Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title_short | Thuto: Depth Analysis of South African and Sierra Leone School Outcomes using Machine Learning |
| title_sort | thuto depth analysis of south african and sierra leone school outcomes using machine learning |
| topic | UCTD Education Policy-making and Machine learning |
| url | http://hdl.handle.net/2263/83192 |