Education is commonly seen as an escape from poverty and a critical path to securing a better standard of living. This is especially relevant in the South African context, where the need is so great that in one instance people were trampled to death at the gates of a higher educational institution, whilst attempting to register for this opportunity. The root cause of this great need is a limited capacity and a demand, which outstrips the supply. This is not a problem specific to South Africa. It is however exaggerated in the South African context due to the country's lack of infrastructure and the opening of facilities to all people. Tertiary educational institutions are faced with ever-increasing applications for a limited number of available positions. This study focuses on a dataset from the Nelson Mandela Metropolitan University's Faculty of Engineering, the Built Environment and Information Technology - with the aim of establishing guidelines for the use of data modelling techniques to improve student admissions criteria. The importance of data preprocessing was highlighted and generalized linear regression, decision trees and neural networks were proposed and motivated for modelling. Experimentation was carried out, resulting in a number of recommended guidelines focusing on the tremendous value of feature engineering coupled with the use of generalized linear regression as a base line. Adding multiple models was highly recommended; since it allows for greater opportunities for added insight.
|Nelson Mandela Metropolitan University, Engineering, the Built Environment and Information technology
|South African National ETD Portal
|Thesis, Masters, MTech
|vi, 161 leaves, pdf
|Nelson Mandela Metropolitan University
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