Genetic algorithm based prediction of students' course performance using learning analytics.
| dc.contributor.advisor | Subramaniam, Prabhakar Rontala. | |
| dc.contributor.advisor | Govender, Irene. | |
| dc.contributor.author | Raghavjee, Rushil. | |
| dc.date.accessioned | 2024-04-22T11:57:01Z | |
| dc.date.available | 2024-04-22T11:57:01Z | |
| dc.date.created | 2024 | |
| dc.date.issued | 2024 | |
| dc.description | Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg. | |
| dc.description.abstract | Learning Analytics (LA) can play a key role in understanding students’ learning and academic performance. By identifying poorly performing students early, LA can also be used to identify students who are at risk of dropping out of programmes. This enables academic advisors to intervene early and provide help to ensure students stay on track and succeed in their studies. Hence, LA is becoming a common trend in education particularly in higher education. Previous studies of LA have not dealt with specific courses in information systems and information technology. Therefore, the aim of this study was to develop a model for the application of LA to different courses with the discipline of Information Systems and Technology using various data sources. This study used the design science research approach to help towards solving the problem of understanding students’ learning and performance in Higher Education Institutions (HEIs). Multiple data sources were used. The data that was obtained was pre-processed using MS Excel. Thereafter, the WEKA tool was used in the analysis of the data and prediction of performance. Decision tree, Random Forest and genetic-based algorithms were used to develop prediction models for each of the courses in the discipline of Information Systems and Technology at the University of KwaZulu-Natal. The study also resulted in the development of an integrated dataset for the discipline of Information Systems and Technology in higher education and a process model for the implementation of LA in a specific discipline. The involvedness of the data allows future researchers to continuously improve/evolve the area of LA. This study should, therefore, be of value to LA practitioners wishing to implement LA to courses within other disciplines as well. | |
| dc.identifier.doi | https://doi.org/10.29086/10413/22941 | |
| dc.identifier.uri | https://hdl.handle.net/10413/22941 | |
| dc.language.iso | en | |
| dc.rights | CC0 1.0 Universal | en |
| dc.rights.uri | http://creativecommons.org/publicdomain/zero/1.0/ | |
| dc.subject.other | Learning analytics. | |
| dc.subject.other | Applications of learning analytics. | |
| dc.subject.other | Benefits of learning analytics. | |
| dc.subject.other | Challenges of learning analytics. | |
| dc.title | Genetic algorithm based prediction of students' course performance using learning analytics. | |
| dc.type | Thesis |