Optimization- and AI-based approaches to academic quality quantification for transparent academic recruitment: part 1-model development

TL;DR

This paper develops two computational frameworks, an optimization-based and a Siamese neural network-based, to quantify academic quality through a single index called AQI, aiding transparent academic recruitment.

Contribution

It introduces novel optimization and neural network models for academic quality quantification, creating a decision-support tool for fair recruitment processes.

Findings

Models produce a unified Academic Quality Index (AQI).

Reference data from top-ranked universities used for model tuning.

Frameworks enable transparent and fair academic quality assessment.

Abstract

For fair academic recruitment at universities and research institutions, determination of the right measure based on globally accepted academic quality features is a highly delicate, challenging, but quite important problem to be addressed. In a series of two papers, we consider the modeling part for academic quality quantification in the first paper, in this paper, and the case studies part in the second paper. For academic quality quantification modeling, we develop two computational frameworks which can be used to construct a decision-support tool: (i) an optimization-based framework and (ii) a Siamese network (a type of artificial neural network)-based framework. The output of both models is a single index called Academic Quality Index (AQI) which is a measure of the overall academic quality. The data of academics from first-class and average-class world universities, based on Times Higher Education World University Rankings and QS World University Rankings, are assumed as the reference data for tuning model parameters.