Predicting grade progression within the Limpopo Education System
| dc.contributor.advisor | Berman, Sonia | |
| dc.contributor.author | Ramphele, Frans | |
| dc.date.accessioned | 2019-05-15T10:55:08Z | |
| dc.date.available | 2019-05-15T10:55:08Z | |
| dc.date.issued | 2018 | |
| dc.date.updated | 2019-05-15T10:54:32Z | |
| dc.description.abstract | One way to improve education in South Africa is to ensure that additional support and resourcing are provided to schools and learners that are most in need of help. To this end, education officials need to understand the factors affecting learning and the schools most in need of appropriate interventions. Several theories, models and methods have been developed to attempt to address the challenges faced in the education sector. Educational Data Mining (EDM) is one which has gained prominence in addressing these challenges. EDM is a field of data mining using mathematical and machine learning models to improve learners’ performance, education administration, and policy formulation. This study explored the literature and related methodologies used within the EDM context and constructed a solution to improve learner support and planning in the Limpopo primary and secondary schools education system. The data utilized included socio-economic environment, demographic information as well as learner’s performance sourced from the Education Management Information Systems database of the Limpopo Department of Education (LDoE). Feature selection methods; Information Gain, Correlation and Asymmetrical Uncertainty were combined to determine factors that affect learning. Three machine learning classifiers, AdaboostM1 (Decision Stump), HoeffdingTree and NaïveBayes, were used to predict learners’ grade progression. These were compared using several evaluation metrics and HoeffdingTree outperformed AdaboostM1 (Decision Stump) and NaïveBayes. When the final HoeffdingTree model was applied to the test datasets, the performance was exceptionally good. It is hoped that the implementation of this model will assist the LDoE in its role of supporting learning and planning of resource allocation. | |
| dc.identifier.apacitation | Ramphele, F. (2018). <i>Predicting grade progression within the Limpopo Education System</i>. (). ,Faculty of Science ,Department of Computer Science. Retrieved from http://hdl.handle.net/11427/30137 | en_ZA |
| dc.identifier.chicagocitation | Ramphele, Frans. <i>"Predicting grade progression within the Limpopo Education System."</i> ., ,Faculty of Science ,Department of Computer Science, 2018. http://hdl.handle.net/11427/30137 | en_ZA |
| dc.identifier.citation | Ramphele, F. 2018. Predicting grade progression within the Limpopo Education System. . ,Faculty of Science ,Department of Computer Science. http://hdl.handle.net/11427/30137 | en_ZA |
| dc.identifier.ris | TY - Thesis / Dissertation AU - Ramphele, Frans AB - One way to improve education in South Africa is to ensure that additional support and resourcing are provided to schools and learners that are most in need of help. To this end, education officials need to understand the factors affecting learning and the schools most in need of appropriate interventions. Several theories, models and methods have been developed to attempt to address the challenges faced in the education sector. Educational Data Mining (EDM) is one which has gained prominence in addressing these challenges. EDM is a field of data mining using mathematical and machine learning models to improve learners’ performance, education administration, and policy formulation. This study explored the literature and related methodologies used within the EDM context and constructed a solution to improve learner support and planning in the Limpopo primary and secondary schools education system. The data utilized included socio-economic environment, demographic information as well as learner’s performance sourced from the Education Management Information Systems database of the Limpopo Department of Education (LDoE). Feature selection methods; Information Gain, Correlation and Asymmetrical Uncertainty were combined to determine factors that affect learning. Three machine learning classifiers, AdaboostM1 (Decision Stump), HoeffdingTree and NaïveBayes, were used to predict learners’ grade progression. These were compared using several evaluation metrics and HoeffdingTree outperformed AdaboostM1 (Decision Stump) and NaïveBayes. When the final HoeffdingTree model was applied to the test datasets, the performance was exceptionally good. It is hoped that the implementation of this model will assist the LDoE in its role of supporting learning and planning of resource allocation. DA - 2018 DB - OpenUCT DP - University of Cape Town LK - https://open.uct.ac.za PY - 2018 T1 - Predicting grade progression within the Limpopo Education System TI - Predicting grade progression within the Limpopo Education System UR - http://hdl.handle.net/11427/30137 ER - | en_ZA |
| dc.identifier.uri | http://hdl.handle.net/11427/30137 | |
| dc.identifier.vancouvercitation | Ramphele F. Predicting grade progression within the Limpopo Education System. []. ,Faculty of Science ,Department of Computer Science, 2018 [cited yyyy month dd]. Available from: http://hdl.handle.net/11427/30137 | en_ZA |
| dc.language.rfc3066 | eng | |
| dc.publisher.department | Department of Computer Science | |
| dc.publisher.faculty | Faculty of Science | |
| dc.title | Predicting grade progression within the Limpopo Education System | |
| dc.type | Master Thesis | |
| dc.type.qualificationlevel | Masters | |
| dc.type.qualificationname | MPhil |