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EĞİTSEL VERİ MADENCİLİĞİ İLE İLGİLİ 2006-2016 YILLARI ARASINDA YAPILAN ÇALIŞMALARIN İNCELENMESİ

Yıl 2018, Cilt: 8 Sayı: 2, 108 - 124, 15.07.2018
https://doi.org/10.17943/etku.351473

Öz

Veri madenciliği mevcut verileri analiz
etmede, ilişkileri çıkarmada ve eldeki verilerden anlamlı bilgiler ortaya
çıkarmada kullanılan bir tekniktir. Veri madenciliği sayesinde elle açığa
çıkarılması zor olan ve zaman alan gizli bilgiler daha kolay bir şekilde açığa
çıkarılmaktadır. Bu sebeplerle günümüzde veri madenciliğine yönelik araştırmaların
sayısı artmıştır. Veri madenciliği birçok alanda olduğu gibi eğitim alanında da
kullanılmaktadır. Eğitim sistemleriyle ilgili araştırmaların artmasıyla Eğitsel
Veri Madenciliği alanına yönelen bir araştırma topluluğu ortaya çıkmıştır. Eğitim
alanında; öğrencilerin öğrenme davranışları, öğretim, rehberlik, yönetim,
öğrencilerin başarı durumları, okuldan ayrılma nedenleri, seçmeli ders
seçimleri gibi çalışmalara alanyazında rastlanmıştır. Bu çalışmada 2006-2016
yılları arasında eğitsel veri madenciliği ile ilgili yayınlanmış olan
çalışmalar incelenmiştir. 
Eğitsel veri
madenciliği alanı ile ilgili yayınların yer aldığı düşünülen yedi farklı
veritabanındaki makaleler, belirlenen ölçütler kapsamında taranmıştır. İncelenen
çalışmalar, yayın yılı, araştırma konusu, veri türü, çalışma grubu, veri
toplama araçları vb. ölçütlere göre
betimsel istatistikî yöntemlerle analiz edilmiştir.
Araştırma bulgularına göre, çalışmaların çoğunun araştırma konusu akademik
başarı ve öğrenci performansıdır. Yine araştırma bulgularına göre, çalışma
grubunu çoğunlukla lise ve üniversite öğrencilerinin oluşturduğu görülmektedir.
Elde edilen sonuçların
gelecek çalışmalara ışık tutacağı düşünülmektedir.

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Yıl 2018, Cilt: 8 Sayı: 2, 108 - 124, 15.07.2018
https://doi.org/10.17943/etku.351473

Öz

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  • Ocumpaugh, J., Baker, R., Gowda, S., Heffernan, N., & Heffernan, C. (2014). Population validity for Educational Data Mining models: A case study in affect detection. British Journal of Educational Technology, 45(3), 487-501.
  • Osmanbegović, E., & Suljić, M. (2012). Data mining approach for predicting student performance. Economic Review, 10(1).
  • Pal, S. (2012). Mining educational data to reduce dropout rates of engineering students. International Journal of Information Engineering and Electronic Business, 4(2), 1.
  • Pandey, U. K., & Pal, S. (2011). Data Mining: A prediction of performer or underperformer using classification. arXiv preprint arXiv:1104.4163.
  • Pandey, U. K., & Pal, S. (2011). A Data mining view on class room teaching language. arXiv preprint arXiv:1104.4164.
  • Papamitsiou, Z. K., & Economides, A. A. (2014). Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Educational Technology & Society, 17(4), 49-64.
  • Park, Y., Yu, J. H., & Jo, I. H. (2016). Clustering blended learning courses by online behavior data: A case study in a Korean higher education institute. The Internet and Higher Education, 29, 1-11.
  • Patel, M. B., & Dharwa, J. (2016). Selection of Optimal Classification Algorithms in Education Data Mining. Imperial Journal of Interdisciplinary Research, 3(1).
  • Petcu, N. (2015). Data mining techniques used to analyze students' opinions about computization in the educational system. Bulletin of the Transilvania University of Brasov. Economic Sciences. Series V, 8(1), 289.
  • Priya, K. S., & Kumar, A. S. (2013). Improving the student's performance using educational data mining. International Journal of Advanced Networking and Applications, 4(4), 1806.
  • Rabbany, R., Takaffoli, M., & Zaïane, O. R. (2011). Analyzing participation of students in online courses using social network analysis techniques. In Proceedings of educational data mining.
  • Rajshree, M., & Arya, S. (2011). Role of Data Mining in Minimizing Socio-Economic Risk Factor (SERF) Affecting Agriculture. International Journal of Advanced Research in Computer Science, 2(5).
  • Ramaswami, M., & Bhaskaran, R. (2009). A study on feature selection techniques in educational data mining. arXiv preprint arXiv:0912.3924.
  • Reimann, P., Markauskaite, L., & Bannert, M. (2014). e‐Research and learning theory: What do sequence and process mining methods contribute?. British Journal of Educational Technology, 45(3), 528-540.
  • Rice, K., & Hung, J. L. (2015). Data Mining in Online Professional Development Program Evaluation: An Exploratory Case Study. International Journal of Technology in Teaching & Learning.
  • Sachin, R. B., & Vijay, M. S. (2012, January). A survey and future vision of data mining in educational field. In Advanced Computing & Communication Technologies (ACCT), 2012 Second International Conference on (pp. 96-100). IEEE.
  • Sahu, A. K. (2016). The Criticism of Data Mining Applications and Methodologies. International Journal of Advanced Research in Computer Science, 7(1).
  • Santos, O. C., & Boticario, J. G. (2015). User‐centred design and educational data mining support during the recommendations elicitation process in social online learning environments. Expert Systems, 32(2), 293-311.
  • Saranya, A., & Rajeswari, J. (2016). Enhanced Prediction Of Student Dropouts Usıng Fuzzy Inference System And Logıstıc Regressıon. Ictact Journal On Soft Computing, 6(2).
  • Scheffel, M., Drachsler, H., Stoyanov, S., & Specht, M. (2014). Quality Indicators for Learning Analytics. Educational Technology & Society, 17(4), 117-132.
  • Sevindik, T., Kayışlı, K., ve Ünlükahraman, O. (2012). Web Tabanlı Eğitimde Veri Madenciliği. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 3(3).
  • Sin, K., & Muthu, L. (2015). Application of big data in education data mining and learning analytics—A lterature review. ICTACT Journal on Soft Computing, 5(4), 1-035.
  • Soares, F., Machado, C., Diniz, D., Maciel, A., & Rodrigues, R. (2016, November). Educational Data Mining to support Distance Learning students with difficulties in the Portuguese Grammar. In Brazilian Symposium on Computers in Education (Simpósio Brasileiro de Informática na Educação-SBIE) (Vol. 27, No. 1, p. 956).
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  • Stephen, K. W. (2016). Data Mining Model for Predicting Student Enrolment in STEM Courses in Higher Education Institutions.
  • Şengür, D., ve Tekin, A. (2013). Öğrencilerin Mezuniyet Notlarının Veri Madenciliği Metotları İle Tahmini. Internatıonal Journal Of Informatıcs Technologıes, 6(3), 7-16.
  • Şuşnea, E. (2011). Data mining techniques used in on-line military training. In Conference proceedings of» eLearning and Software for Education «(eLSE) (No. 01, pp. 201-205). Universitatea Nationala de Aparare Carol I.
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  • Tekin, A. (2014). Early Prediction of Students' Grade Point Averages at Graduation: A Data Mining Approach. Eurasian Journal of Educational Research, 54, 207-226. Thai-Nghe, N., Drumond, L., Krohn-Grimberghe, A., & Schmidt-Thieme, L. (2010). Recommender system for predicting student performance. Procedia Computer Science, 1(2), 2811-2819.
  • Thuneberg, H., & Hotulainen, R. (2006). Contributions of data mining for psycho‐educational research: what self‐organizing maps tell us about the well‐being of gifted learners. High Ability Studies, 17(1), 87-100.
  • Tsai, Y. R., Ouyang, C. S., & Chang, Y. (2016). Identifying engineering students’ English sentence reading comprehension Errors: applying a data mining technique. Journal of Educational Computing Research, 54(1), 62-84.
  • Udupi, P. K., Sharma, N., & Jha, S. K. (2016, September). Educational data mining and big data framework for e-learning environment. In Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO), 2016 5th International Conference on (pp. 258-261). IEEE.
  • Valsamidis, S., Kontogiannis, S., Kazanidis, I., Theodosiou, T., & Karakos, A. (2012). A Clustering Methodology of Web Log Data for Learning Management Systems. Educational Technology & Society, 15(2), 154-167.
  • Wang, J., & Li, L. (2016). Research on the College Graduate Employment Education Based on Data Mining Technology. ANTHROPOLOGIST, 23(1-2), 231-235.
  • Winne, P. H., & Baker, R. S. (2013). The potentials of educational data mining for researching metacognition, motivation and self-regulated learning. JEDM-Journal of Educational Data Mining, 5(1), 1-8
  • Wu, C., Mai, F., & Yu, Y. (2015). Teaching Data Mining to Business Undergraduate Students Using R. Business Education Innovation Journal,7(2).
  • Yadav, S. K., & Pal, S. (2012). Data mining: A prediction for performance improvement of engineering students using classification. arXiv preprint arXiv:1203.3832.
  • You, J. W. (2016). Identifying significant indicators using LMS data to predict course achievement in online learning. The Internet and Higher Education, 29, 23-30.7
  • Yukselturk, E., Ozekes, S., ve Türel, Y. K. (2014). Predicting dropout student: an application of data mining methods in an online education program. European Journal of Open, Distance and E-learning, 17(1), 118-133.
  • Xu, B., & Recker, M. (2012). Teaching Analytics: A Clustering and Triangulation Study of Digital Library User Data. Educational Technology & Society, 15(3), 103-115.
  • Zain, J. M., & Herawan, T. (2014). Data Mining for Education Decision Support: A Review. International Journal of Emerging Technologies in Learning, 9(6).
  • Zengin, K., Esgi, N., Erginer, E., ve Aksoy, M. E. (2011). A sample study on applying data mining research techniques in educational science: Developing a more meaning of data. Procedia-Social and Behavioral Sciences, 15, 4028-4032.
Toplam 147 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Ahmet Tekin

Zeynep Öztekin

Yayımlanma Tarihi 15 Temmuz 2018
Yayımlandığı Sayı Yıl 2018 Cilt: 8 Sayı: 2

Kaynak Göster

APA Tekin, A., & Öztekin, Z. (2018). EĞİTSEL VERİ MADENCİLİĞİ İLE İLGİLİ 2006-2016 YILLARI ARASINDA YAPILAN ÇALIŞMALARIN İNCELENMESİ. Eğitim Teknolojisi Kuram Ve Uygulama, 8(2), 108-124. https://doi.org/10.17943/etku.351473