Optimization- and AI-based approaches to academic quality quantification for transparent academic recruitment: part 1-model development
This addresses the need for transparent academic recruitment at universities and research institutions, but it appears incremental as it applies existing methods to a new domain.
The paper tackles the problem of quantifying academic quality for fair recruitment by developing two computational frameworks: an optimization-based approach and a Siamese network-based approach, which output a single Academic Quality Index (AQI) using data from world university rankings.
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.