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.
Highlights
Scientometric studies have been shown to be an effective instrument for quantifying various metrics by analyzing the information extracted from the related data(sets) [1]
OF Times Higher Education (THE) STUDY Since this article analyses a world university ranking from a scientometric point of view based on contrast patterns, this section reviews several prominent world university rankings (Section II-A) and the concepts related to pattern-based classification (Section II-B)
SciVal was selected because it is based on Scopus and allows for obtaining several metrics related to the authors and institutions, such as collaboration impact, field-weighted citation impact (FWCI), publications in top journal percentiles, and scholarly output, which are not found in the Scopus database
Summary
Scientometric studies have been shown to be an effective instrument for quantifying various metrics by analyzing the information extracted from the related data(sets) [1]. The potential of scientometric studies is partly evidenced by the increasing number of related publications – see Fig. 1. It is not surprising that universities are finding ways to increase their attractiveness to prospective students (and researchers alike), such as by placing higher in university rankings [1]. This is partly evidenced by the observation that a large number of top universities have dedicated webpages on their websites listing their positions in the various university rankings (e.g., Australian National University, Nanyang Technological University in Singapore, and Massachusetts Institute of Technology in the U.S.6). Having a better position in the world university rankings can increase the attractiveness of the universities to funding agencies, prospective researchers and faculty members, and other universities seeking to collaborate on both students
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