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University Rankings and Goals: A Cluster Analysis

Author

Listed:
  • Roba Elbawab

    (BRU-IUL, Instituto Universitário de Lisboa (ISCTE-IUL), Avenida das Forças Armadas, Edificio ISCTE, 1649-026 Lisbon, Portugal)

Abstract

Nowadays, university rankings are used to assess all aspects of universities. Due to the impact of university rankings on assessing the performance of universities, this research aims to explore university rankings in depth. University rankings are considered contributors to assessing university performance. Previous literature showed different types of goals, such as output and support goals, where the literature advised to align between these two types of goals. Universities have different goals, but still, university rankings measure all universities on the same criteria. Subsequently, this research has used the most used university rankings in the literature, QS world ranking dataset. Then unsupervised machine learning was performed to cluster the universities. The results divided universities among four clusters. This study helps in allocating the university in the adequate cluster. This study helps university managers define the goals of their universities. The study recommends universities align their support goals with their output goals. The study recommends universities to develop international goals and strategies, and support the research in the universities by supporting the scholars. This study’s novelty lies in connecting the university rankings and goals using management analytics in education.

Suggested Citation

  • Roba Elbawab, 2022. "University Rankings and Goals: A Cluster Analysis," Economies, MDPI, vol. 10(9), pages 1-34, August.
    • Handle: RePEc:gam:jecomi:v:10:y:2022:i:9:p:209-:d:902190
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    References listed on IDEAS

    as
    1. Maria Adamakou & Dimitris Kallioras & George Petrakos, 2021. "Detecting Convergence Trends among EU Universities," Economies, MDPI, vol. 9(2), pages 1-8, March.
    2. Xin Gu & Karen L. Blackmore, 2019. "Developing a scholar classification scheme from publication patterns in academic science: A cluster analysis approach," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 70(11), pages 1262-1276, November.
    3. Qi Chen & Wen Luo & Gregory J. Palardy & Ryan Glaman & Amber McEnturff, 2017. "The Efficacy of Common Fit Indices for Enumerating Classes in Growth Mixture Models When Nested Data Structure Is Ignored," SAGE Open, , vol. 7(1), pages 21582440177, March.
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