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.

Keywords

• University rankings
• data mining
• scientometrics
• contrast patterns