Journals / Yükseköğretim Dergisi / 2026 / Cilt: 16 Sayı: 1

Evaluation of Academic Performance of Universities Established in Türkiye after 2000 with Louvain Cluster Analysis

Türkiye’de 2000 Sonrası Kurulan Üniversitelerin Akademik Performanslarının Louvain Kümeleme Analizi ile Değerlendirilmesi

İbrahim Budak(Kastamonu Üniversitesi)0000-0001-7762-6114
Pages
221–234
Publication Date
2026-03-17
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The study provides a strategic framework for improving the current performance of universities and emphasizes the need to support low-performing universities and encourage international cooperation.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In higher education, academic performance metrics have become a fundamental resource in universities’ strategic planning and quality assessments.
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Key Findings & Data:However, the methods used in these rankings need to be analyzed in more detail and the current performance needs to be improved strategically.
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Conclusion & Impact:The study provides a strategic framework for improving the current performance of universities and emphasizes the need to support low-performing universities and encourage international cooperation.
🔬Design:Araştırma Makalesi
📍Scope:2000 Verileri
⚙️Variables:Üniversite Sıralamaları · Louvain Kümeleme Analizi · Akademik Performans

Abstract

In higher education, academic performance metrics have become a fundamental resource in universities’ strategic planning and quality assessments. The rankings that reveal the success levels of universities are generally determined by parameters based on international standards. However, the methods used in these rankings need to be analyzed in more detail and the current performance needs to be improved strategically. The aim of this study is to classify the academic performance of universities established after 2000 in Türkiye using the Louvain clustering method based on University Ranking by Academic Performance (URAP) indicators. In the study, universities were divided into six different clusters (C1-C6) by analyzing data for the years 2022-2024. These clusters were determined according to indicators such as academic productivity, research capacity and international cooperation. The Louvain method allows universities to be classified according to their similar characteristics and clearly reveals differences in performance. The analysis shows that universities in the Marmara and Aegean regions are concentrated at the top, while those in the Eastern and Southeastern Anatolia regions are concentrated at the bottom. The study provides a strategic framework for improving the current performance of universities and emphasizes the need to support low-performing universities and encourage international cooperation.

Özet

Yükseköğretimde akademik performans metrikleri, üniversitelerin stratejik planlamalarında ve kalite değerlendirmelerinde temel bir kaynak haline gelmiştir. Üniversitelerin başarı seviyelerini ortaya koyan sıralamalar, genellikle uluslararası standartlara dayalı parametrelerle belirlenmektedir. Ancak bu sıralamalarda kullanılan metotların daha detaylı analiz edilmesi ve mevcut performansın stratejik bir şekilde geliştirilmesi gerekmektedir. Bu çalışmanın amacı, Türkiye’de 2000 yılından sonra kurulan üniversitelerin akademik performanslarını University Ranking by Academic Performance (URAP) sıralamaları üzerinden değerlendirerek Louvain kümelenme yöntemiyle sınıflandırmaktadır. Çalışmada, 2022-2024 yıllarına ait veriler analiz edilerek üniversiteler altı farklı kümeye (C1-C6) ayrılmıştır. Bu kümeler, üniversitelerin akademik üretkenlik, araştırma kapasitesi ve uluslararası işbirliği gibi göstergelere göre belirlenmiştir. Louvain yöntemi, üniversitelerin benzer özelliklerine göre sınıflandırılmasını sağlayarak performans farklılıklarını net bir şekilde ortaya koymaktadır. Analizler, Marmara ve Ege bölgelerindeki üniversitelerin üst sıralarda, Doğu ve Güneydoğu Anadolu bölgelerindekilerin ise daha alt sıralarda yoğunlaştığını göstermektedir. Çalışma, üniversitelerin mevcut performanslarını artırmaya yönelik stratejik bir çerçeve sunarken düşük performanslı üniversitelerin desteklenmesi ve uluslararası işbirliklerinin teşvik edilmesi gerektiğini vurgulamaktadır.

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Cite This Article

Budak, İ. (2026). Evaluation of Academic Performance of Universities Established in Türkiye after 2000 with Louvain Cluster Analysis. Yükseköğretim Dergisi, Cilt: 16 Sayı: 1, 221–234. https://doi.org/10.53478/yuksekogretim.1595511

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References (26)

  1. Abramo, G., D’Angelo, C. A., & Di Costa, F. (2011). National research assessment exercises: The effects of changing the rules of the game during the game. Scientometrics, 88(1), 229-238.
  2. Banász, Z., Kosztyán, Z. T., Csányi, V. V., & Telcs, A. (2022). University leagues alongside rankings. Quality & Quantity, 57(1), 721–736.
  3. Blondel, V. D., Guillaume, J. L., Lambiotte, R., & Lefebvre, E. (2008). Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment, 2008(10), P10008.
  4. Erdogmus, N., & Esen, M. (2016). Classifying universities in Turkey by hierarchical cluster analysis. Education and Science, 41(184), 363–382.
  5. Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanaraman, A., Lu, H., Chavarria-Miranda, D.,. & Gebremedhin, A. H. (2018). Distributed Louvain Algorithm for Graph Community Detection. IEEE International Parallel and Distributed Processing Symposium, 885-895.
  6. Gopal, K., & Shitan, M. (2015, May). Cluster analysis of top 200 universities in mathematics. In 2015 International Symposium on Mathematical Sciences and Computing Research (iSMSC) (pp. 408-413). IEEE.
  7. Held, P., Krause, B., & Kruse, R. (2016). Dynamic Clustering in social networks using Louvain and infomap method. Third European Network Intelligence Conference, 61-69.
  8. Hou, A. Y. C., Morse, R., & Chiang, C. L. (2012). An analysis of mobility in global rankings: Making institutional strategic plans and positioning for building world-class universities. Higher Education Research & Development, 31(6), 841–857.
  9. Lambrechts, A. A., & Lepori, B. (2023). Changes in higher education funding: Performance-based funding in the US and Europe. In Handbook on Higher Education Management and Governance (pp. 275-292). Edward Elgar Publishing.
  10. Luque-Martínez, T., & Luque-Raya, I. (2024). Spanish scientific research by field and subject: Strategic analysis with ARWU indicators. Scientometrics, 129, 5265–5285.
  11. Mambetkaziyev, A., Baikenov, Z., & Konopyanova, G. (2022). Cluster analysis of the effectiveness of management of higher education institutions. Eastern-European Journal of Enterprise Technologies, 6(3), 26–31.
  12. Marjai, P., Szabari, B., & Kiss, A. (2021). An experimental study on centrality measures using clustering. Computers, 10(9), 115.
  13. Matlis, G., Dimokas, N., & Karvelis, P. (2023). A Clustering-Based Approach for Ranking Universities. IEEE. https://doi.org/10.1109/IISA59645.2023.
  14. Murniyati, M., Mutiara, A. B., Wirawan, S., Yusnitasari, T., & Anggraini, D. (2023). Expanding Louvain algorithm for clustering relationship formation. International Journal of Advanced Computer Science and Applications, 14(1), 701-704.
  15. Nolle, L. (2010). Cluster-based benchmarking of universities as an alternative to league tables. In M. Bramer et al. (Eds.), Research and Development in Intelligent Systems XXVI (pp. 1–10). Springer.
  16. Perchinunno, P., & Cazzolle, M. (2020). A clustering approach for classifying universities in a world sustainability ranking. Environmental Impact Assessment Review, 85, 106471.
  17. Polat, S. (2017). The expansion of higher education in Turkey: Access, equality and regional returns to education. Structural Change and Economic Dynamics, 43, 1-14.
  18. Polyakov, M., Bilozubenko, V., Korneyev, M., & Nebaba, N. (2020). Analysis of key university leadership factors based on their international rankings (QS and THE). Problems and Perspectives in Management, 18(4), 142–152.
  19. Raponi, V., Martella, F., & Maruotti, A. (2015). A biclustering approach to university performances: An Italian case study. Journal of Applied Statistics, 43(1), 31–45.
  20. Stanciu, I. D., & Nistor, N. (2024). Doctoral capstone theories as indicators of university rankings: Insights from a machine learning approach. Computers in Human Behavior, 164, 108504.
  21. TÜİK. (2023). Bölgesel İstatistikler: Eğitim ve Ekonomik Göstergeler. Türkiye İstatistik Kurumu. Retrieved from November 28, 2024 from https://data.tuik.gov.tr
  22. URAP (2024). Retrieved from November 28, 2024 from https://newtr.urapcenter.org/
  23. Urueta, D. E., Lara, P., Gutiérrez, M. Á., de-los-Cobos, S. G., Rincón, E. A., & Mora, R. A. (2021). A comparative ranking model among Mexican universities using pattern recognition. Mathematics, 9(14), 1615.
  24. Wang, S., & Koopman, R. (2017). Clustering articles based on semantic similarity. Scientometrics, 111, 1017-1031.
  25. YÖK (2025). Retrieved from July 24, 2025 from https://www.yok.gov.tr/
  26. Ghosh, S., Halappanavar, M., Tumeo, A., Kalyanaraman, A., Lu, H., Chavarria-Miranda, D.,. & Gebremedhin, A. H. (2018). Distributed Louvain Algorithm for Graph Community Detection. IEEE International Parallel and Distributed Processing Symposium, 885-895.