Dissertation/ Thesis
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Using machine learning and agent-based simulation to predict learner progress for the South African high school education system

Λεπτομέρειες βιβλιογραφικής εγγραφής
Τίτλος: Using machine learning and agent-based simulation to predict learner progress for the South African high school education system
Συγγραφείς: Van den Heever, Maymarie
Συνεισφορές: Venter, Lieschen, Bekker, James, Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
Στοιχεία εκδότη: Stellenbosch University
Έτος έκδοσης: 2024
Συλλογή: Stellenbosch University: SUNScholar Research Repository
Θεματικοί όροι: Education -- South Africa -- Evaluation, Machine learning -- Computer simulation, School improvement programs -- South Africa -- Data processing, Multiagent systems -- South Africa, UCTD
Περιγραφή: 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 Unpublished doctoral dissertation.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 ...
Τύπος εγγράφου: thesis
Περιγραφή αρχείου: xii, 120 pages; application/pdf
Γλώσσα: unknown
Relation: https://scholar.sun.ac.za/handle/10019.1/131943
Διαθεσιμότητα: https://scholar.sun.ac.za/handle/10019.1/131943
Rights: Stellenbosch University
Αριθμός Καταχώρησης: edsbas.26F08AF9
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  Data: Venter, Lieschen<br />Bekker, James<br />Stellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.
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  Data: 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 ...
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      – SubjectFull: Education -- South Africa -- Evaluation
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      – SubjectFull: Machine learning -- Computer simulation
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      – SubjectFull: School improvement programs -- South Africa -- Data processing
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      – SubjectFull: Multiagent systems -- South Africa
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      – TitleFull: Using machine learning and agent-based simulation to predict learner progress for the South African high school education system
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