Open Access

ARTICLE

Google Scholar University Ranking Algorithm to Evaluate the Quality of Institutional Research

Noor Ul Sabah¹, Muhammad Murad Khan^1,*, Ramzan Talib¹, Muhammad Anwar², Muhammad Sheraz Arshad Malik³, Puteri Nor Ellyza Nohuddin⁴

1 Department of Computer Science, Government College University, Faisalabad 38000, Pakistan
2 Department of Information Sciences, Division of Science and Technology, University of Education, Lahore, 54000, Pakistan
3 Department of Software Engineering, Government College University, Faisalabad 38000, Pakistan
4 Institute of IR4.0, Universiti Kebangsaan Malaysia, Bangi, Selangor, 43600, Malaysia

* Corresponding Author: Muhammad Murad Khan. Email:

Computers, Materials & Continua 2023, 75(3), 4955-4972. https://doi.org/10.32604/cmc.2023.037436

Received 03 November 2022; Accepted 17 February 2023; Issue published 29 April 2023

Abstract

Education quality has undoubtedly become an important local and international benchmark for education, and an institute’s ranking is assessed based on the quality of education, research projects, theses, and dissertations, which has always been controversial. Hence, this research paper is influenced by the institutes ranking all over the world. The data of institutes are obtained through Google Scholar (GS), as input to investigate the United Kingdom’s Research Excellence Framework (UK-REF) process. For this purpose, the current research used a Bespoke Program to evaluate the institutes’ ranking based on their source. The bespoke program requires changes to improve the results by addressing these methodological issues: Firstly, Redundant profiles, which increased their citation and rank to produce false results. Secondly, the exclusion of theses and dissertation documents to retrieve the actual publications to count for citations. Thirdly, the elimination of falsely owned articles from scholars’ profiles. To accomplish this task, the experimental design referred to collecting data from 120 UK-REF institutes and GS for the present year to enhance its correlation analysis in this new evaluation. The data extracted from GS is processed into structured data, and afterward, it is utilized to generate statistical computations of citations’ analysis that contribute to the ranking based on their citations. The research promoted the predictive approach of correlational research. Furthermore, experimental evaluation reported encouraging results in comparison to the previous modification made by the proposed taxonomy. This paper discussed the limitations of the current evaluation and suggested the potential paths to improve the research impact algorithm.

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