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Data mining and artificial intelligence studies in the field of education in Turkey

Yıl 2021, Cilt: 13 Sayı: 2, 81 - 89, 31.08.2021

Öz

Data mining and artificial intelligence techniques are applied in the field of education as well as in many areas and offer innovations in education and training activities. With the widespread use of data mining and artificial intelligence in education, scientific research has been carried out on these issues. The aim of this study to examine studies on educational data mining and artificial intelligence use and to identify trends in this field. In this context, a literature review was made and the data were analyzed by descriptive analysis method. In order to determine the articles to be used in the study, researches were conducted with keywords such as education, data mining, educational data mining, learning analytics, artificial intelligence, intelligent learning systems, and adaptive learning environments in TR Index, Dergipark and Google Scholar searchs and indexing engines and full text articles on the subject were examined. As a result of the searches, a total of 112 articles were included in the study and these articles were analyzed within the framework of the Article Review Form.

Kaynakça

  • [1] Hand DJ, Adams NM Data Mining. Editörler: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL. Major Reference Works. Wiley StatsRef: Statistics Reference Online. 2015.
  • [2] Wu X, Zhu X, Wu G, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107, 2014. doi: 10.1109/TKDE.2013.109.
  • [3] Han J, Kamber M, Pei J. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, Burlington, 2012.
  • [4] Gartner Glossary. https://www.gartner.com/en/information-technology/glossary (Erişim Tarihi: 23.05.2020).
  • [5] Dunham MH. Data mining introductory and advanced topics. Upper Saddle River, NJ: Pearson Education, Inc., 2003.
  • [6] Romero C, Ventura S. Data mining in education. WIREs Data Mining Knowl Discov, 3, 12-27, 2013. doi:10.1002/widm.1075
  • [7] Al Mazidi A, Abusham E. Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches. International Journal of Engineering Business Management, 10, 1–11, 2018.
  • [8] Saa AA. Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212-220, 2016.
  • [9] Keskin S, Aydın F, Yurdugül H. Eğitsel Veri Madenciliği Ve Öğrenme Analitikleri Bağlamında E-Öğrenme Verilerinde Aykırı Gözlemlerin Belirlenmesi. EĞİTİM TEKNOLOJİSİ Kuram ve Uygulama, 9(1), 292-309, 2019.
  • [10] Chatti MA, Dyckhoff AL, Schroeder U, Thüs H. A Reference Model for Learning Analytics. International Journal of Technology Enhanced Learning, 4(5), 318-331, 2012. doi: 10.1504/IJTEL.2012.051816
  • [11] Chassignol M, Khoroshavin A, Klimova A, Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24, 2018.
  • [12] Baker RSJD, Yacef K. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 2009.
  • [13] Mohamad SK, Tasir Z. Educational Data Mining: A Review. Procedia - Social and Behavioral Sciences, 97, 320-324, 2013.
  • [14] Papamitsiou Z, Economides AA. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Journal of Educational Technology & Society, 17(4), 49–64, 2014.
  • [15] Shahiri AM, Husain W, Rashid NA. A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422, 2015.
  • [16] Sin K, Muthu L. Application of big data in educational data mining and learning analytics - a literature review. ICTAC Journal of Soft Computing, 5(4), 1035-1049, 2015.
  • [17] Sukhija K, Jindal M, Aggarwal N. The recent state of educational data mining: A survey and future visions. 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE), Amritsar, 1-2 October 2015.
  • [18] Dutt A, Ismail MA, Herawan T. A Systematic Review on Educational Data Mining. IEEE Access, 5, 15991-16005, 2017.
  • [19] Kumar M, Singh AJ, Handa D. Literature Survey on Student’s Perfor-mance Prediction in Education using Data Mining Techniques. I.J. Education and Management Engineering, 6, 40-49, 2017.
  • [20] Tekin A, Öztekin Z. Eğitsel Veri Madenciliği İle İlgili 2006-2016 Yılları Arasında Yapılan Çalışmaların İncelenmesi. Eğitim Teknolojisi Kuram ve Uygu-lama, 8(2), 108-124, 2018. doi: 10.17943/etku.351473
  • [21] Chassignol M, Khoroshavin A, Klimova A, Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24, 2018.
  • [22] Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators?. International Journal of Educational Technology in Higher Education, 16(39), 2019.
  • [23] Chen L, Chen P, Lin Z. Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278, 2020. doi: 10.1109/ACCESS.2020.2988510.
  • [24] Yıldırım A, Şimşek H. Sosyal Bilimlerde Nitel Araştırma Yöntemleri. 8th Ed. Seçkin Yayınevi, Ankara, Türkiye, 2011.
  • [25] Worldcloud Generator. https://www.wordclouds.com (Erişim Tarihi: 23.05.2020).
  • [26] Özbay Ö, Ersoy H. Öğrenme Yönetim Sistemi Üzerindeki Öğrenci Hareketliliğinin Veri Madenciliği Yöntemleriyle Analizi. GEFAD / GUJGEF, 37(2), 523-558, 2017.

Türkiye’de eğitim alanında yapılan veri madenciliği ve yapay zeka çalışmaları

Yıl 2021, Cilt: 13 Sayı: 2, 81 - 89, 31.08.2021

Öz

Veri madenciliği ve yapay Zekâ teknikleri birçok alanda olduğu gibi eğitim alanında da uygulanmakta ve eğitim-öğretim faaliyetlerinde yenilikler sunmaktadır. Veri madenciliğinin ve yapay Zekânın eğitimde kullanımının yaygınlaşmasıyla birlikte bilimsel araştırmalar da yapılmaktadır. Bu çalışmanın amacı, Türkiye’de yapılan ve eğitimde veri madenciliği ve yapay Zekâ kullanımını ele alan çalışmaları incelemek ve bu konudaki eğilimleri tespit etmektir. Bu doğrultuda alanyazın taraması yapılmış ve veriler betimsel analizi yöntem ile analiz edilmiştir. Çalışmada kullanılacak makaleleri belirlemek amacıyla TR Dizin, Dergipark ve Google Akademik tarama ve indeksleme motorlarında; eğitim, veri madenciliği, eğitsel veri madenciliği, öğrenme analitikleri, yapay zekâ, zeki öğrenme sistemleri, uyarlanabilir öğrenme ortamları gibi anahtar kelimeler temel alınarak taramalar yapılmış ve konuyla ilgili tam metin olarak ulaşılan makaleler incelemeye alınmıştır. Yapılan taramalar neticesinde toplam 112 adet makale çalışmaya dahil edilmiş ve bu makaleler, hazırlanan makale inceleme formu çerçevesinde analiz edilmiştir.

Kaynakça

  • [1] Hand DJ, Adams NM Data Mining. Editörler: Balakrishnan N, Colton T, Everitt B, Piegorsch W, Ruggeri F, Teugels JL. Major Reference Works. Wiley StatsRef: Statistics Reference Online. 2015.
  • [2] Wu X, Zhu X, Wu G, Ding W. Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97-107, 2014. doi: 10.1109/TKDE.2013.109.
  • [3] Han J, Kamber M, Pei J. Data Mining Concepts and Techniques. Morgan Kaufmann Publishers, Burlington, 2012.
  • [4] Gartner Glossary. https://www.gartner.com/en/information-technology/glossary (Erişim Tarihi: 23.05.2020).
  • [5] Dunham MH. Data mining introductory and advanced topics. Upper Saddle River, NJ: Pearson Education, Inc., 2003.
  • [6] Romero C, Ventura S. Data mining in education. WIREs Data Mining Knowl Discov, 3, 12-27, 2013. doi:10.1002/widm.1075
  • [7] Al Mazidi A, Abusham E. Study of general education diploma students’ performance and prediction in Sultanate of Oman, based on data mining approaches. International Journal of Engineering Business Management, 10, 1–11, 2018.
  • [8] Saa AA. Educational Data Mining & Students’ Performance Prediction. International Journal of Advanced Computer Science and Applications, 7(5), 212-220, 2016.
  • [9] Keskin S, Aydın F, Yurdugül H. Eğitsel Veri Madenciliği Ve Öğrenme Analitikleri Bağlamında E-Öğrenme Verilerinde Aykırı Gözlemlerin Belirlenmesi. EĞİTİM TEKNOLOJİSİ Kuram ve Uygulama, 9(1), 292-309, 2019.
  • [10] Chatti MA, Dyckhoff AL, Schroeder U, Thüs H. A Reference Model for Learning Analytics. International Journal of Technology Enhanced Learning, 4(5), 318-331, 2012. doi: 10.1504/IJTEL.2012.051816
  • [11] Chassignol M, Khoroshavin A, Klimova A, Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24, 2018.
  • [12] Baker RSJD, Yacef K. The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining, 1(1), 2009.
  • [13] Mohamad SK, Tasir Z. Educational Data Mining: A Review. Procedia - Social and Behavioral Sciences, 97, 320-324, 2013.
  • [14] Papamitsiou Z, Economides AA. Learning Analytics and Educational Data Mining in Practice: A Systematic Literature Review of Empirical Evidence. Journal of Educational Technology & Society, 17(4), 49–64, 2014.
  • [15] Shahiri AM, Husain W, Rashid NA. A Review on Predicting Student's Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414-422, 2015.
  • [16] Sin K, Muthu L. Application of big data in educational data mining and learning analytics - a literature review. ICTAC Journal of Soft Computing, 5(4), 1035-1049, 2015.
  • [17] Sukhija K, Jindal M, Aggarwal N. The recent state of educational data mining: A survey and future visions. 2015 IEEE 3rd International Conference on MOOCs, Innovation and Technology in Education (MITE), Amritsar, 1-2 October 2015.
  • [18] Dutt A, Ismail MA, Herawan T. A Systematic Review on Educational Data Mining. IEEE Access, 5, 15991-16005, 2017.
  • [19] Kumar M, Singh AJ, Handa D. Literature Survey on Student’s Perfor-mance Prediction in Education using Data Mining Techniques. I.J. Education and Management Engineering, 6, 40-49, 2017.
  • [20] Tekin A, Öztekin Z. Eğitsel Veri Madenciliği İle İlgili 2006-2016 Yılları Arasında Yapılan Çalışmaların İncelenmesi. Eğitim Teknolojisi Kuram ve Uygu-lama, 8(2), 108-124, 2018. doi: 10.17943/etku.351473
  • [21] Chassignol M, Khoroshavin A, Klimova A, Bilyatdinova A. Artificial Intelligence trends in education: a narrative overview. Procedia Computer Science, 136, 16-24, 2018.
  • [22] Zawacki-Richter O, Marín VI, Bond M, Gouverneur F. Systematic review of research on artificial intelligence applications in higher education – where are the educators?. International Journal of Educational Technology in Higher Education, 16(39), 2019.
  • [23] Chen L, Chen P, Lin Z. Artificial Intelligence in Education: A Review. IEEE Access, 8, 75264-75278, 2020. doi: 10.1109/ACCESS.2020.2988510.
  • [24] Yıldırım A, Şimşek H. Sosyal Bilimlerde Nitel Araştırma Yöntemleri. 8th Ed. Seçkin Yayınevi, Ankara, Türkiye, 2011.
  • [25] Worldcloud Generator. https://www.wordclouds.com (Erişim Tarihi: 23.05.2020).
  • [26] Özbay Ö, Ersoy H. Öğrenme Yönetim Sistemi Üzerindeki Öğrenci Hareketliliğinin Veri Madenciliği Yöntemleriyle Analizi. GEFAD / GUJGEF, 37(2), 523-558, 2017.
Toplam 26 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Derleme Makaleler
Yazarlar

Emine Aruğaslan 0000-0002-8153-9117

Hanife Çivril 0000-0003-2925-3688

Yayımlanma Tarihi 31 Ağustos 2021
Yayımlandığı Sayı Yıl 2021 Cilt: 13 Sayı: 2

Kaynak Göster

IEEE E. Aruğaslan ve H. Çivril, “Türkiye’de eğitim alanında yapılan veri madenciliği ve yapay zeka çalışmaları”, UTBD, c. 13, sy. 2, ss. 81–89, 2021.

Dergi isminin Türkçe kısaltması "UTBD" ingilizce kısaltması "IJTS" şeklindedir.

Dergimizde yayınlanan makalelerin tüm bilimsel sorumluluğu yazar(lar)a aittir. Editör, yardımcı editör ve yayıncı dergide yayınlanan yazılar için herhangi bir sorumluluk kabul etmez.