Beyond Rankings: A K-means Approach to Evaluating Research Universities in Emerging Higher Education Systems DOI Creative Commons
Tarık Küçükdeniz,

Melike Solak Altuntas,

Ceyhun Akarsu

и другие.

International Journal of Applied Mathematics Computational Science and Systems Engineering, Год журнала: 2025, Номер 7, С. 51 - 63

Опубликована: Март 27, 2025

This study introduces a novel approach to evaluating research universities in developing countries, using Türkiye as case within the broader context of global higher education trends. By combining national University Ranking by Academic Performance (URAP-TR) metrics with K-means clustering analysis, we address limitations international ranking systems assessing institutions outside Global North. Our comparative analysis 23 Turkish universities, implemented Python and scikit-learn, resulted three distinct clusters that reflect diverse patterns institutional development. allows for nuanced comparison university performance Turkey's landscape, while also connecting debates on rankings metrics. A focused examination Istanbul University-Cerrahpasa illustrates how this method can inform targeted improvement strategies, offering insights applicable similar contexts worldwide. moving beyond traditional rankings, facilitates data-driven decision-making policy strategy.

Язык: Английский

Beyond Rankings: A K-means Approach to Evaluating Research Universities in Emerging Higher Education Systems DOI Creative Commons
Tarık Küçükdeniz,

Melike Solak Altuntas,

Ceyhun Akarsu

и другие.

International Journal of Applied Mathematics Computational Science and Systems Engineering, Год журнала: 2025, Номер 7, С. 51 - 63

Опубликована: Март 27, 2025

This study introduces a novel approach to evaluating research universities in developing countries, using Türkiye as case within the broader context of global higher education trends. By combining national University Ranking by Academic Performance (URAP-TR) metrics with K-means clustering analysis, we address limitations international ranking systems assessing institutions outside Global North. Our comparative analysis 23 Turkish universities, implemented Python and scikit-learn, resulted three distinct clusters that reflect diverse patterns institutional development. allows for nuanced comparison university performance Turkey's landscape, while also connecting debates on rankings metrics. A focused examination Istanbul University-Cerrahpasa illustrates how this method can inform targeted improvement strategies, offering insights applicable similar contexts worldwide. moving beyond traditional rankings, facilitates data-driven decision-making policy strategy.

Язык: Английский

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