Automated Matchmaking to Improve Accuracy of Applicant Selection for University Education System
July 09, 2015 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
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Authors
Oludayo O. Olugbara, Manish Joshi, Michael M. Modiba, Virendrakumar C. Bhavsar
arXiv ID
1507.02439
Category
cs.AI: Artificial Intelligence
Cross-listed
cs.CY
Citations
1
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The accurate applicant selection for university education is imperative to ensure fairness and optimal use of institutional resources. Although various approaches are operational in tertiary educational institutions for selecting applicants, a novel method of automated matchmaking is explored in the current study. The method functions by matching a prospective students skills profile to a programmes requisites profile. Empirical comparisons of the results, calculated by automated matchmaking and two other selection methods, show matchmaking to be a viable alternative for accurate selection of applicants. Matchmaking offers a unique advantage that it neither requires data from other applicants nor compares applicants with each other. Instead, it emphasises norms that define admissibility to a programme. We have proposed the use of technology to minimize the gap between students aspirations, skill sets and course requirements. It is a solution to minimize the number of students who get frustrated because of mismatched course selection.
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