FRAMEWORK FOR IDENTIFYING THE MOST LIKELY SUCCESSFUL UNDERPRIVILEGED TERTIARY STUDY BURSARY APPLICANTS

Authors

  • Renier Steynberg Department of Industrial Engineering, Stellenbosch University, South Africa
  • Danie Lötter Department of Industrial Engineering, Stellenbosch University, South Africa
  • Jan Harm van Vuuren Department of Industrial Engineering, Stellenbosch University, South Africa

DOI:

https://doi.org/10.7166/28-2-1695

Keywords:

Decision support, statistical learning, classification, ensemble models, multi-criteria decision analysis, tertiary study bursaries

Abstract

In this paper, a decision support system framework is proposed that may be used to assist a tertiary bursary provider during the process of allocating bursaries to prospective students. The system identifies those in an initial pool of applicants who are expected to be successful tertiary students, to facilitate final selection from a shortlist of candidates. The working of the system is based on various classification models for predicting whether bursary applicants will be successful in their respective tertiary studies. These model predictions are then combined in a weighted fashion to produce a final prediction for each student. In addition, a multi-criteria decision analysis method is used to assign each of the applicants to a ranking level. In this way, the system suggests both a predicted outcome for each candidate and a ranking according to which candidates may be compared. The practical working of the system is demonstrated in the context of real data provided by an industry partner, and the success rate of the system’s recommendations is compared with that of the industry partner.

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Published

2017-09-04

How to Cite

Steynberg, R., Lötter, D., & van Vuuren, J. H. (2017). FRAMEWORK FOR IDENTIFYING THE MOST LIKELY SUCCESSFUL UNDERPRIVILEGED TERTIARY STUDY BURSARY APPLICANTS. The South African Journal of Industrial Engineering, 28(2), 59–77. https://doi.org/10.7166/28-2-1695

Issue

Vol. 28 No. 2 (2017)

Section

General Articles

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