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Using machine learning and agent-based simulation to predict learner progress for the South African high school education system
Van den Heever, Maymarie. 2025. Using machine learning and agent-based simulation to predict learner progress for the South African high school education system. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/handle/10019.1/131...
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Stellenbosch : Stellenbosch University
2025
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| _version_ | 1867613964410552320 |
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| access_status_str | Open Access |
| author | Van den Heever, Maymarie |
| author2 | Venter, Lieschen |
| author_browse | Van den Heever, Maymarie Venter, Lieschen |
| author_facet | Venter, Lieschen Van den Heever, Maymarie |
| author_sort | Van den Heever, Maymarie |
| collection | Thesis |
| dc_rights_str_mv | Stellenbosch University |
| description | Van den Heever, Maymarie. 2025. Using machine learning and agent-based simulation to predict learner progress for the South African high school education system. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/handle/10019.1/131943 |
| format | Thesis |
| id | oai:scholar.sun.ac.za:10019.1/131943 |
| institution | Stellenbosch University (South Africa) |
| last_indexed | 2026-06-10T12:44:30.757Z |
| license_str | Other — see source repository |
| provenance_str_mv | Harvested via OAI-PMH from SUNScholar — Stellenbosch University Repository |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | Stellenbosch : Stellenbosch University |
| publisherStr | Stellenbosch : Stellenbosch University |
| record_format | dspace |
| source_str | SUNScholar — Stellenbosch University Repository |
| spelling | oai:scholar.sun.ac.za:10019.1/131943 Using machine learning and agent-based simulation to predict learner progress for the South African high school education system Van den Heever, Maymarie Venter, Lieschen Bekker, James Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering. Education -- South Africa -- Evaluation Machine learning -- Computer simulation School improvement programs -- South Africa -- Data processing Multiagent systems -- South Africa UCTD Van den Heever, Maymarie. 2025. Using machine learning and agent-based simulation to predict learner progress for the South African high school education system. Unpublished doctoral dissertation. Stellenbosch: Stellenbosch University [online]. Available: https://scholar.sun.ac.za/handle/10019.1/131943 Thesis (MEng)--Stellenbosch University, 2024. ENGLISH ABSTRACT: The South African high school education system faces numerous challenges, including high dropout rates and unequal educational outcomes, calling for innovative methods to analyse and address these problems. This study employs an integrated approach that merges machine learning and agent-based modelling to simulate learner progression in public high schools, illuminating the critical factors that influence educational outcomes. Using data from the 2019 General Household Survey in South Africa, factor analysis is first conducted to identify and quantify the principal characteristics defining learners. These features then train an XGBoost machine learning model, which is integrated within an agent-based framework to simulate learner progression from Grades 8 to Grade 12. Validating the model against the Learner Unit Record Information and Tracking System dataset resulted in a root square error of 2.95%, which is indicative of the model’s ability to predict learner progression. Overall, the model represents a significant advancement in the field of educational simulation, serving as a practical tool for schools to analyse and improve learner outcomes through analytical decision-making. AFRIKAANSE OPSOMMING: Die Suid-Afrikaanse hoërskoolonderwysstelsel staar talle uitdagings in die gesig, insluitend hoë uitvalsyfers en ongelyke onderwysuitkomste, wat vra vir innoverende metodes om hierdie probleme te ontleed en aan te spreek. Hierdie studie gebruik ’n geïntegreerde benadering wat masjienleer en agent-gebaseerde modellering saamsmelt om leerdervordering in publieke hoërskole te simuleer. Deur gebruik te maak van data van die 2019 Algemene Huishoudelike Opname in Suid-Afrika, word faktorontleding eers gedoen om die hoofkenmerke wat leerders definieer, te identifiseer. Hierdie faktore word gebruik om ’n XGBoostmasjienleermodel op te lei, wat geïntegreer word binne ’n agent-gebaseerde raamwerk om leerdervordering van Graad 8 tot Graad 12 te simuleer. Die validering van die model teen die LURITS-datastel het gelei tot ’n 2.95% wortel van gemiddeldekwadraatfout, wat ’n aanduiding is van die model se doeltreffende vermoë om leerdervordering te voorspel. Ten slot som lewer die model ’n beduidende bydrae tot die gebied van opvoedkundige simulasie deur te dien as ’n praktiese hulpmiddel vir skole om leerderuitkomste te ontleed en te verbeter deur analitiese besluitneming. Masters 2025-04-30T09:32:30Z 2025-04-30T09:32:30Z 2024-12 Thesis https://scholar.sun.ac.za/handle/10019.1/131943 Stellenbosch University xii, 120 pages application/pdf Stellenbosch : Stellenbosch University |
| spellingShingle | Education -- South Africa -- Evaluation Machine learning -- Computer simulation School improvement programs -- South Africa -- Data processing Multiagent systems -- South Africa UCTD Van den Heever, Maymarie Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title | Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title_full | Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title_fullStr | Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title_full_unstemmed | Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title_short | Using machine learning and agent-based simulation to predict learner progress for the South African high school education system |
| title_sort | using machine learning and agent based simulation to predict learner progress for the south african high school education system |
| topic | Education -- South Africa -- Evaluation Machine learning -- Computer simulation School improvement programs -- South Africa -- Data processing Multiagent systems -- South Africa UCTD |
| url | https://scholar.sun.ac.za/handle/10019.1/131943 |
| work_keys_str_mv | AT vandenheevermaymarie usingmachinelearningandagentbasedsimulationtopredictlearnerprogressforthesouthafricanhighschooleducationsystem |