SavedAdd your journal

Journals / Bayburt Eğitim Fakültesi Dergisi / 2026 / Cilt: 20 Sayı: 47

Bibliometric Analysis of Research on the Use of Data Mining in the Field of Education

Veri Madenciliğinin Eğitim Alanında Kullanımına İlişkin Araştırmaların Bibliyometrik Analizi

Fatih Balaman(Mustafa Kemal Üniversitesi - Eğiitm Fakültesi)0000-0003-2175-0778
Sevil Hanbay Tiryaki(TC MİLLİ EĞİTİM BAKANLIĞI GAZİANTEP)0000-0003-4780-9715
Pages
1088–1115
Publication Date
2025-09-17
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

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

According to the results of the analysis, the journal with the highest number of publications on data mining in the field of educational sciences was Education and Information Technologies.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:The aim of this study is to examine the studies on the use of data mining in the field of education by bibliometric analysis method.
📊
Key Findings & Data:According to the results of the trend subject analysis conducted to determine the trend in the field, it was concluded that the subjects studied increased and the concepts changed after 2013, and differentiated subjects such as e-learning, learning management systems, machine learning, behavior analysis were studied in 2015 and later.
💡
Conclusion & Impact:According to the results of the analysis, the journal with the highest number of publications on data mining in the field of educational sciences was Education and Information Technologies.
🔬Design:Modeling & Simulation
📍Scope:2013 Verileri
⚙️Variables:Eğitimsel veri madenciliği · büyük veri · örüntü

Abstract

The aim of this study is to examine the studies on the use of data mining in the field of education by bibliometric analysis method. According to the results of the analysis, the journal with the highest number of publications on data mining in the field of educational sciences was Education and Information Technologies. While Xing is the author with the highest number of publications, Romero is the author with the highest number of citations. The country with the highest number of publications and citations is the USA. According to the results of the trend subject analysis conducted to determine the trend in the field, it was concluded that the subjects studied increased and the concepts changed after 2013, and differentiated subjects such as e-learning, learning management systems, machine learning, behavior analysis were studied in 2015 and later. In 2024, it was concluded that the trending topics in the field are artificial neural networks, cart decision tree and collaborative filtering. Nevertheless, with the thematic domain analysis, it was concluded that concepts such as “e-learning”, “cloud computing”, “big data”, “educational data mining”, “learning analytics”, “clustering”, “classification”, which are important for the field, are not sufficiently developed and can be further studied.

Özet

Bu çalışmanın amacı veri madenciliğinin eğitim alanında kullanımına ilişkin yapılmış çalışmaların bibliyometrik analiz yöntemi ile incelenmesidir. Analiz sonuçlarına göre eğitim bilimleri alanında veri madenciliği ile ilgili en fazla yayın yapan dergi Education and Information Technologies’dir. En fazla sayıda yayın yapan yazar Xing iken en çok atıf alan yazar Romero’dur. En çok yayın sayısına ve atıfa sahip ülke ABD’dir. Alandaki eğilimi belirlemek için yapılan trend konu analizi sonuçlarına göre ise 2013 sonrasında çalışılan konuların arttığı ve kavramların değiştiği, 2015 yılı ve sonrasında e-öğrenme, öğrenme yönetim sistemleri, makine öğrenimi, davranış analizi, gibi farklılaşan konuların çalışıldığı anlaşılmıştır. 2024 yılının alandaki trend konularının ise yapay sinir ağları, cart karar ağacı ve iş birliğine dayalı filtreleme olduğu sonucuna ulaşılmıştır. Ayrıca yapılan tematik alan analizi ile alan için önemli olan “e-öğrenme”, “bulut bilişim”, “büyük veri”, “eğitimsel veri madenciliği”, “öğrenme analitikleri”, “kümeleme” ve “sınıflandırma” gibi kavramların yeterince gelişmemiş olduğu tespit edilmiştir. Araştırmacılara bu alanlarda çalışma yapmaları önerilmektedir.

🔖

Cite This Article

Balaman, F., Tiryaki, S. H. (2025). Bibliometric Analysis of Research on the Use of Data Mining in the Field of Education. Bayburt Eğitim Fakültesi Dergisi, Cilt: 20 Sayı: 47, 1088–1115. https://doi.org/10.35675/befdergi.1605165

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (41)

  1. Arruda, H., Silva, E. R., Lessa, M., Proença Jr, D., & Bartholo, R. (2022). VOSviewer and bibliometrix. Journal of the Medical Library Association: JMLA, 110(3), 392. https://doi.org/10.5195/jmla.2022.1434
  2. Baek, C., & Doleck, T. (2022). Educational data mining: A bibliometric analysis of an emerging field. in IEEE Access, vol. 10, pp. 31289-31296, https://doi.org/10.1109/ACCESS.2022.3160457
  3. Boz, M., & Şimşek, İ. (2022). Milli Eğitim Bakanlığı eğitim yönetim bilgi sistemlerinin birlikte çalışabilirlik açısından incelenmesi. Journal of Qualitative Research in Education, 32, 213-240. https://doi.org/10.14689/enad.32.1702
  4. Bozkurt, A. (2016). Öğrenme analitiği: e-öğrenme, büyük veri ve bireyselleştirilmiş öğrenme. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 2(4), 55-81. https://dergipark.org.tr/en/download/article-file/402115
  5. Boztaş, G. D., Berigel, M., & Altınay, F. (2024). A bibliometric analysis of Educational Data Mining studies in global perspective. Education and Information Technologies, 29, 8961–8985. https://doi.org/10.1007/s10639-023-12170-0
  6. Broadus, R. N. (1987). Toward a definition of “bibliometrics”. Scientometrics, 12, 373-379.
  7. Bütüner, R., & Calp, M. H. (2022). Estimation of the academic performance of students in distance education using data mining methods. International Journal of Assessment Tools in Education, 9(2), 410-429. https://doi.org/10.21449/ijate.904456
  8. Cobo, M.J., López Herrera, A.G., Herrera-Viedma, E. & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the Fuzzy Sets Theory field. Journal of Informetrics, 5(1), 146-166. https://doi.org/10.1016/j.joi.2010.10.002
  9. Çelik, K. (2021). Bulut bilişim teknolojileri. Bartın Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 12(24), 436-450. https://doi.org/10.47129/bartiniibf.1019898
  10. Çelik, K., Güleryüz, S., & Özköse, H. (2018). 4. Endüstri devrimine kuramsal bakış. Avrasya Sosyal ve Ekonomi Araştırmaları Dergisi (ASEAD), 5(9), 86-95.
  11. Deng, L. (2022). Developing countermeasures of integrating entrepreneurship education with professional education in colleges and universities using data mining. Journal of Electrical and Computer Engineering, 2023(1), 1-10. https://doi.org/10.1155/2022/1723712
  12. Dereli, A. B. (2024). Vosviewer ile Bibliyometrik Analiz. Communicata, 28, 1-7. https://doi.org/10.32952/communicata.1517725
  13. Diwani, S. A., & Sam, A. (2014). Data mining awareness and readiness in healthcare sector: A case of Tanzania. Advances in Computer Science: an International Journal, 3(1), 37-43.
  14. Feng, L. (2021). Research on higher education evaluation and decision‐making based on data mining. Scientific Programming, 2021(1), 1-9. https://doi.org/10.1155/2021/6195067
  15. Gaudioso, E., & Talavera, L. (2006). Data mining to support tutoring in virtual learning communities: Experiences and challenges. Data mining in e-learning/Wit Press.
  16. Gupta, N. (2021). Trends in IoT research: A bibliometric and science mapping analysis of internet of things. Library Philosophy and Practice, 1-20. https://digitalcommons.unl.edu/libphilprac/5269
  17. Gülleroğlu, H. D., & Coşkun, F. (2024). Büyük verinin yapısı ve eğitimde kullanılması. Millî Eğitim, 53(242), 1139-1152. https://doi.org/10.37669/milliegitim.1222977
  18. Hall, C. M. (2011). Publish and perish? Bibliometric analysis, journal ranking and the assessment of research quality in tourism. Tourism management, 32(1), 16-27. https://doi.org/10.1016/j.tourman.2010.07.001
  19. Hamal, O., El Faddouli, N. E., Harouni, M. H. A., & Lu, J. (2022). Artificial intelligent in education. Sustainability, 14(5), 1-11. https://doi.org/10.3390/su14052862
  20. Han, J., Kamber, M., & Pei, J. (2006). Data Mining: Concepts and Techniques. Morgan Kaufmann. Hotaman, D. (2020). Öğrenci başarısının değerlendirilmesinde eğitsel veri madenciliğinin kullanımı. Ulakbilge Sosyal Bilimler Dergisi, 8(48), 577-587. https://doi.org/10.7816/ulakbilge-08-48-08
  21. Joshi, A. (2016). Comparison between Scopus & ISI Web of science. Journal Global Values, 7(1), 1-11. Kızrak, M. A., & Bolat, B. (2018). Derin öğrenme ile kalabalık analizi üzerine detaylı bir araştırma. Bilişim Teknolojileri Dergisi, 11(3), 263-286. https://doi.org/10.17671/gazibtd.419205
  22. Köklü, N., & Sulak, S. A. (2024). Recent developments in educational data mining: A four-year bibliometric analysis. Advances in Education Sciences, M. Dalkılıç and O. Soslu (Ed.) Platanus Publishing, 5-29.
  23. Li, X., Nie, Y., Chen, M., Liu, X., & Liu, X. (2014). Members' behavior in virtual learning community: A study using data mining approach. Computer and Information Science, 7(4), 1-8. https://doi.org/10.5539/cis.v7n4p1
  24. Magdin, M., & Turcáni, M. (2015). Personalization of student in course management systems on the basis using method of data mining. Turkish Online Journal of Educational Technology-TOJET, 14(1), 58-67. https://doi.org/10.1186/s41239-024-00486-x
  25. Mokhtari, H., Mirezati, S. Z., Saberi, M. K., Fazli, F., & Kharabati-Neshin, M. (2019). A bibliometric analysis and visualization of the scientific publications of universities: A study of hamadan university of medical sciences during 1992-2018. Webology, 16(2), 187-211. https://doi.org/10.31219/osf.io/92u3m
  26. Mostow, J., Beck, J., Cen, H., Cuneo, A., Gouvea, E., & Heiner, C. (2005). An educational data mining tool to browse tutor-student interactions: Time will tell. Proceedings of the Workshop on Educational Data Mining, National Conference on Artificial Intelligence, USA. https://www.cs.cmu.edu/~listen/pdfs/AAAI2005-WS205MostowJ.pdf
  27. Özbay, Ö. (2015). Veri madenciliği kavrami ve eğitimde veri Madenciliği uygulamalari. Uluslararası Eğitim Bilimleri Dergisi, 2(5), 262-272. https://doi.org/10.16991/INESJOURNAL.162
  28. Özdemir, A., Saylam, R., & Bilen, B. B. (2018). Eğitim sisteminde veri madenciliği uygulamaları ve farkındalık üzerine bir durum çalışması. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 22(2), 2159-2172.
  29. Pai, K. C., Kuo, B. C., Liao, C. H., & Liu, Y. M. (2021). An application of Chinese dialogue-based intelligent tutoring system in remedial instruction for mathematics learning. Educational Psychology, 41(2), 137-152. https://www.tandfonline.com/doi/abs/10.1080/01443410.2020.1731427
  30. Riehmann, P., Hanfler, M., & Froehlich, B. (2005, 23-25 October). Interactive sankey diagrams. IEEE Symposium on Information Visualization - INFOVIS. (pp. 233-240). Minneapolis, USA. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=1532152
  31. Romero, C., & Ventura, S. (2013). Data mining in education. Wiley Interdisciplinary Reviews: Data mining and knowledge discovery, 3(1), 12-27. https://doi.org/10.1002/widm.1075
  32. Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. https://doi.org/10.1016/j.compedu.2007.05.016
  33. Savaş, S., Topaloğlu, N., & Yılmaz, M. (2012). Veri madenciliği ve Türkiye’deki uygulama örnekleri. İstanbul Ticaret Üniversitesi Fen Bilimleri Dergisi, 11(21), 1-23. https://dergipark.org.tr/tr/download/article-file/199592
  34. Schmidhuber, J. (2015). Deep learning in neural networks: An overview. Neural Networks, 61, 85–117. https://doi.org/10.1016/j.neunet.2014.09.003
  35. Şimşek Gürsoy, U. T. (2009). Veri madenciliği ve bilgi keşfi. Pegem Akademi
  36. Tosunoğlu, E., Yılmaz, R., Özeren, E., & Sağlam, Z. (2021). Eğitimde makine öğrenmesi: araştırmalardaki güncel eğilimler üzerine inceleme. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 3(2), 178-199. https://dergipark.org.tr/en/download/article-file/1873550
  37. Wang, C. (2021). Analysis of students’ behavior in english online education based on data mining. Mobile Information Systems, 2021(11), 1-10. https://doi.org/10.1155/2021/1856690
  38. Wang, C., Dai, J., & Xu, L. (2022). Big data and data mining in education: A Bibliometrics study from 2010 to 2022. 2022 7th International Conference on Cloud Computing and Big Data Analytics (ICCCBDA). Chengdu, China, pp. 507-512. https://doi.org/10.1109/ICCCBDA55098.2022.9778874
  39. Yang, L., Zheng, S., Xu, X., Sun, Y., Wang, X., & Li, J. (2021). Medical data mining course development in postgraduate medical education: web-based survey and case study. JMIR Medical Education, 7(4), 1-18. https://doi.org/10.2196/24027
  40. Yörük, T. (2024). Eğitimde yapay zeka ve kişiselleştirilmiş öğrenme. S. Karataş (Ed.), Eğitim Bilimleri Alanında Uluslararası Araştırmalar XXIII, Eğitim Yayınevi.
  41. Yuan, X. (2022). Network education resource information sharing system based on data mining. Mathematical Problems in Engineering, 2022(1), 1-8. https://doi.org/10.1155/2022/4080049