Automated Matchmaking to Improve Accuracy of Applicant Selection for University Education System

TL;DR

This paper introduces an automated matchmaking method for university applicant selection, matching student skills to program requirements, showing improved accuracy and fairness over traditional methods.

Contribution

The study presents a novel automated matchmaking approach that does not compare applicants but focuses on matching individual skills to program norms, enhancing selection accuracy.

Findings

Matchmaking outperforms traditional selection methods in accuracy.

The approach reduces applicant frustration due to better course fit.

It does not require applicant comparison, ensuring fairness.

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.