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

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dc.contributor.advisorVenter, Lieschenen_ZA
dc.contributor.advisorBekker, Jamesen_ZA
dc.contributor.authorvan den Heever, Maymarieen_ZA
dc.contributor.otherStellenbosch University. Faculty of Engineering. Dept. of Industrial Engineering.en_ZA
dc.date.accessioned2025-04-30T09:32:30Z
dc.date.available2025-04-30T09:32:30Z
dc.date.issued2024-12
dc.descriptionThesis (MEng)--Stellenbosch University, 2024.en_ZA
dc.description.abstractThe 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.en_ZA
dc.description.versionMastersen_ZA
dc.format.extent133 pagesen_ZA
dc.identifier.urihttps://scholar.sun.ac.za/handle/10019.1/131943
dc.publisherStellenbosch : Stellenbosch Universityen_ZA
dc.rights.holderStellenbosch Universityen_ZA
dc.titleUsing machine learning and agent-based simulation to predict learner progress for the South African high school education systemen_ZA
dc.typeThesisen_ZA
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Graduation - 2024 - December (Open Access)