A Contrast Pattern-Based Scientometric Study of the QS World University Ranking

Abstract

Despite the shortcomings and criticisms of world university rankings, such metrics are widely used by students and parents to select institutions and by educational institutions to attract talented students and researchers, as well as funding. This article introduces the first contrast pattern-based scientometric study of world university rankings. Specifically, this study collects a database containing 34 features, which describe the essential research indicators for the top 200 universities in the Quacquarelli Symonds (QS) ranking. The use of 18 state-of-the-art classifiers in this database shows that the top 100 universities in the QS World University Rankings are separable from the remaining compared universities, achieving an average accuracy of 71%. Additionally, using a contrast pattern mining algorithm, a set of patterns describing the top 100 universities is extracted based on scientometric features. Additionally, this study proposes an approach for visualizing the extracted patterns to facilitate the decision-makers, such as senior university managers, in formulating and evaluating their research (ranking) strategies.INDEX TERMS University rankings, data mining, scientometrics, contrast patterns.

Research Productivity in Terms of Output, Impact, and Collaboration for University Researchers in Saudi Arabia: SciVal Analytics and t-Tests Statistical Based Approach

“…With the inclusion of such scientometric studies for the students and faculties, the academic entity gains recognition in terms of Quacquarelli Symonds (QS) ranking [16] and Times Higher Education ranking. Attaining, maintaining, and improving the QS ranking is not an easy assignment.…”

Design of evaluation system for higher vocational institutions based on AHP hierarchical analysis

“…The focus of research is mainly on universities and the evaluation of their professional, research, teaching and management aspects. In the area of institutional evaluation, OCTAVIO proposes a visual data extraction method to analysis the developmental changes of institutions [1], and Samuel Gratzl proposes a method of comparing multi-attribute rankings to compare the rankings of universities in different years [2]. This paper uses AHP hierarchical analysis to discover and study the comprehensive strength of the collected higher vocational institutions, and further improve the evaluation system through significance and consistency tests, with a view to providing a scientific basis for the planning and construction of higher vocational institutions in the future.…”

Mixed-Integer Programming Model for Ranking Universities: Letting Universities Choose the Weights

“…This is clearly reflected when ranking positions determine policies related to the restructuring of the higher education system, as in the French case, where different universities were merged to create a university with a high ranking [6]. Despite the shortcomings and the criticism of the university rankings, they are still widely used by students and their parents to select institutions, and by educational institutions to attract talented students and researcher, as well as funding [24]. According to [4], funding explains up to 51% of the variability of the positions attained by the universities in some rankings.…”

“…The composite I-distance indicator methodology as an alternative to weighting was proposed in [12]. In [24], the authors used a contrast pattern mining algorithm to extract a set of patterns describing the top 100 universities in the QS World University Rankings and showed, how are these top universities separable from the rest. A robustness analysis, based on a multi-modelling approach, was performed in [32] to test the validity of the inference about the rankings produced in the ARWU, Shanghai, and THE rankings.…”