Araştırma Makalesi
BibTex RIS Kaynak Göster
Yıl 2020, Cilt: 3 Sayı: 1, 1 - 10, 13.07.2020

Öz

Kaynakça

  • [1] Akgün ÖE, Büyüköztürk Ş, Çakmak EK, Demirel F, Karadeniz Ş. Bilimsel Araştırma Yöntemleri. 3.Bölüm. Örnekleme Yöntemleri. 22. Baskı. Ankara: Pegem Akademi, 2008.
  • [2] Akhtar Z, Niazi K. “The relationship between socio-economic status and learning achievement of students at secondary level”. International Journal of Academic Research, 3(2), 956-961, 2011.
  • [3] Altunkaynak B. Veri Madenciliği Yöntemleri ve R Uygulamaları Kavramlar-Modeller-Algoritmalar. 1.Baskı. Seçkin Yayıncılık, Ankara, 2017.
  • [4] Baker RSJD, Yacef K. “The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining”, Article 1, Vol 1, No 1, Fall 2009.
  • [5] Bourdieu P. “Culture reproduction and social reproduction,” in Knowledge, Education, and Cultural Change, Editor: Brown R. London, Tavistock, 1973.
  • [6] Bourdieu P, Passeron JC. “Reproduction in Education, Society and Culture”, Vol. 4, Newbury Park, CA: Sage, 1990.
  • [7] Bousbia N, Belamri I. Which Contribution Does EDM Provide to Computer-Based Learning Environments? Editor: Peña-Ayala A, Educational data mining (s.3-25). Volume, 524, Newyork, Springer, 2014.
  • [8] Breiman L. “Random Forests”. Machine Learning, 45(1), 5-32, 2001 . [9] Coleman, J. S. “Social capital in the creation of human capital”. Am.J. Sociol. 94, S95–S120..doi:10.1086/228943, 1988.
  • [10] Gelbal S. “The effect of socio-economic status of eighth grade students on their achievement”. Turkish Education and Science, 33(150), 1-13, 2008.
  • [11] Gök M. “Makine Öğrenmesi Yöntemleri İle Akademik Başarının Tahmin Edilmesi”. Gazi Üniversitesi Fen Bilimleri Dergisi, Part C, Tasarım Ve Teknoloji, GU J Sci, Part C, 5(3): 139-148, 2017.
  • [12] Gürsoy T. Veri Madenciliğinde Güncel Yaklaşımlar. 1.Baskı, Çağlayan Yayıncılık, İstanbul, 2014.
  • [13] Hämäläinen W, Vinni M. Classifiers for Educational Data Mining. Editors: Romero C, Ventura, S Pechenizkiy, M Baker, RSJD. Handbook of Educational Data Mining, 2011.
  • [14] Hall MA. “Correlation-based Feature Selection for Machine Learning”. Doctoral dissertation, University of Waikato, Dept. of Computer Science, Hamilton, NewZaland, 1999.
  • [15] Jolliffe I. Principal Component Analysis.Editor: Lovric M. International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg, 2014.
  • [16] Kleinbaum DG, Klein M. Logistic Regression. A Self-Learning Text.Third Edition, Springer New York, 2010. [17] Peña-Ayala A. “Educational Data Mining: A survey and a data mining-based analysis of recent works”. Expert systems with applications, 41(4), 1432-1462, 2014. [18] Peña-Ayala A, Cárdenas L. “How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study”. Editor: Peña-Ayala A. Educational data mining, s.65-101, Volume, 524, Newyork, Springer, 2014. [19] Petcu N. “Data mining techniques used to analy ze students opinions about computization in the educational system”. Bulletin of the Transilvania University of Brasov. Economic Sciences. Series V, 8(1), 289, 2015. [20] Pettigrew EJ. “A Study of the impact of scioeconomic status on student achievement in a rural east Tennessee school system”. Electronic Theses and Dissertations. Paper 1844, 2009. [21] Popper K. The Logic Of Scientific Discovery, New York, Routledge, 2005. [22] Romero C, Ventura S. “Educational data mining: a survey from 1995 to 2005”. Expert System with Applications, 33, 135-146, 2007. [23] Romero C, Ventura S, Pechenizkiy M, Baker Ryan SJ d. Handbook of Educational Data Mining. Chapman, Hall/CRC Data Mining and Knowledge Discovery Series, CRC Press, 2011. [24] Sammut C, Webb G. “Encyclopedia of Machine Learning. Cross Validation”, 2010. [25] Silahtaroğlu G. Veri Madenciliği Kavram Ve Algoritmaları. 3.Basım. Papatya Yayıncılık Eğitim, İstanbul, 2016.
  • [26] Steele MB. “Exact bootstrap k-nearest neighbor learners”. Mach Learn, 2009. 74: 235–255 DOI 10.1007/s10994-008-5096-0. [27] Şirin SR. “Socioeconomic status and academic achievement: A meta-analytic review of research”. Review of Educational Research, 75, 417–453,2005. [28] weka.classifiers.meta. http://weka.sourceforge. net/doc.dev/weka/classifiers/meta/package-summary.html (4.06.2019). [29] Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ. Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science, 1999. [30] Wu X, Kumar V, Quinlan JR, Ghosh J, Yang O, Motoda H, McLachlan GJ, Liu B, Yu PS, Zhou Z, Steinbach, M, Hand DJ, Steinberg D, 2007. “10 Algorithms in Data Mining”. Knowledge & Information Systems, Jan 2008, Vol. 14 Issue 1, p1-37, 37p, 4 Diagrams, 2 Graphs; DOI: 10.1007/s10115-007-0114-2. [31] Yu L, Liu H. “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution”. Department of Computer Science & Engineering, Arizona State University, Tempe, AZ 85287-5406, USA, 2003. [32] Zaki MJ, Wagner M. Data Mining and Analysis: Fundamental Concepts and Algorithms. Online (and Offline) Robust PCA, Novel, 2013.

Ortaokul Öğrencilerinin Akademik Başarılarının Eğitsel Veri Madenciliği Yöntemleri İle Tahmini

Yıl 2020, Cilt: 3 Sayı: 1, 1 - 10, 13.07.2020

Öz

Eğitsel Veri Madenciliği, eğitim ortamlarından gelen benzersiz veri türlerini araştırmak için yöntemler geliştirmek, öğrencileri ve öğrendikleri ortamları daha iyi anlamak için bu yöntemleri kullanmakla ilgilenen yeni bir disiplindir. Eğitsel veri madenciliği, bilgisayar bilimi, eğitim ve istatistik alanlarının birleşimi olarak düşünülebilir. Bu çalışmanın amacı, öğrencilerin demografik özelliklerinin ve sosyoekonomik durumlarının öğrencilerin yıl sonu genel başarı ortalamalarına olan etkilerini eğitsel veri madenciliği yöntemleri ile analiz etmektir. Bu amaçla, 2018-2019 eğitim-öğretim yılı, 2.Dönemi’nde, Yalova ilinde sosyodemografik açıdan farklı 4 resmi ortaokuldaki, 5.6.7.8. sınıf, 1395 ortaokul öğrencisinin, E-Okul Yönetim Bilgi Sisteminden sosyodemografik özelliklerine ilişkin verileri elde edilmiştir. Daha sonra elde edilen verilerden sınıflandırma teknikleri ve algoritmaları ile yıl sonu genel başarım ortalamaları tahmin edilmiştir. Sınıflandırıcı algoritmaların uygulanması sonucunda yıl sonu genel başarı ortalaması başarımında lojistik algoritması en iyi tahmini gerçekleştirmiştir.

Kaynakça

  • [1] Akgün ÖE, Büyüköztürk Ş, Çakmak EK, Demirel F, Karadeniz Ş. Bilimsel Araştırma Yöntemleri. 3.Bölüm. Örnekleme Yöntemleri. 22. Baskı. Ankara: Pegem Akademi, 2008.
  • [2] Akhtar Z, Niazi K. “The relationship between socio-economic status and learning achievement of students at secondary level”. International Journal of Academic Research, 3(2), 956-961, 2011.
  • [3] Altunkaynak B. Veri Madenciliği Yöntemleri ve R Uygulamaları Kavramlar-Modeller-Algoritmalar. 1.Baskı. Seçkin Yayıncılık, Ankara, 2017.
  • [4] Baker RSJD, Yacef K. “The State of Educational Data Mining in 2009: A Review and Future Visions. Journal of Educational Data Mining”, Article 1, Vol 1, No 1, Fall 2009.
  • [5] Bourdieu P. “Culture reproduction and social reproduction,” in Knowledge, Education, and Cultural Change, Editor: Brown R. London, Tavistock, 1973.
  • [6] Bourdieu P, Passeron JC. “Reproduction in Education, Society and Culture”, Vol. 4, Newbury Park, CA: Sage, 1990.
  • [7] Bousbia N, Belamri I. Which Contribution Does EDM Provide to Computer-Based Learning Environments? Editor: Peña-Ayala A, Educational data mining (s.3-25). Volume, 524, Newyork, Springer, 2014.
  • [8] Breiman L. “Random Forests”. Machine Learning, 45(1), 5-32, 2001 . [9] Coleman, J. S. “Social capital in the creation of human capital”. Am.J. Sociol. 94, S95–S120..doi:10.1086/228943, 1988.
  • [10] Gelbal S. “The effect of socio-economic status of eighth grade students on their achievement”. Turkish Education and Science, 33(150), 1-13, 2008.
  • [11] Gök M. “Makine Öğrenmesi Yöntemleri İle Akademik Başarının Tahmin Edilmesi”. Gazi Üniversitesi Fen Bilimleri Dergisi, Part C, Tasarım Ve Teknoloji, GU J Sci, Part C, 5(3): 139-148, 2017.
  • [12] Gürsoy T. Veri Madenciliğinde Güncel Yaklaşımlar. 1.Baskı, Çağlayan Yayıncılık, İstanbul, 2014.
  • [13] Hämäläinen W, Vinni M. Classifiers for Educational Data Mining. Editors: Romero C, Ventura, S Pechenizkiy, M Baker, RSJD. Handbook of Educational Data Mining, 2011.
  • [14] Hall MA. “Correlation-based Feature Selection for Machine Learning”. Doctoral dissertation, University of Waikato, Dept. of Computer Science, Hamilton, NewZaland, 1999.
  • [15] Jolliffe I. Principal Component Analysis.Editor: Lovric M. International Encyclopedia of Statistical Science. Springer, Berlin, Heidelberg, 2014.
  • [16] Kleinbaum DG, Klein M. Logistic Regression. A Self-Learning Text.Third Edition, Springer New York, 2010. [17] Peña-Ayala A. “Educational Data Mining: A survey and a data mining-based analysis of recent works”. Expert systems with applications, 41(4), 1432-1462, 2014. [18] Peña-Ayala A, Cárdenas L. “How Educational Data Mining Empowers State Policies to Reform Education: The Mexican Case Study”. Editor: Peña-Ayala A. Educational data mining, s.65-101, Volume, 524, Newyork, Springer, 2014. [19] Petcu N. “Data mining techniques used to analy ze students opinions about computization in the educational system”. Bulletin of the Transilvania University of Brasov. Economic Sciences. Series V, 8(1), 289, 2015. [20] Pettigrew EJ. “A Study of the impact of scioeconomic status on student achievement in a rural east Tennessee school system”. Electronic Theses and Dissertations. Paper 1844, 2009. [21] Popper K. The Logic Of Scientific Discovery, New York, Routledge, 2005. [22] Romero C, Ventura S. “Educational data mining: a survey from 1995 to 2005”. Expert System with Applications, 33, 135-146, 2007. [23] Romero C, Ventura S, Pechenizkiy M, Baker Ryan SJ d. Handbook of Educational Data Mining. Chapman, Hall/CRC Data Mining and Knowledge Discovery Series, CRC Press, 2011. [24] Sammut C, Webb G. “Encyclopedia of Machine Learning. Cross Validation”, 2010. [25] Silahtaroğlu G. Veri Madenciliği Kavram Ve Algoritmaları. 3.Basım. Papatya Yayıncılık Eğitim, İstanbul, 2016.
  • [26] Steele MB. “Exact bootstrap k-nearest neighbor learners”. Mach Learn, 2009. 74: 235–255 DOI 10.1007/s10994-008-5096-0. [27] Şirin SR. “Socioeconomic status and academic achievement: A meta-analytic review of research”. Review of Educational Research, 75, 417–453,2005. [28] weka.classifiers.meta. http://weka.sourceforge. net/doc.dev/weka/classifiers/meta/package-summary.html (4.06.2019). [29] Witten IH, Frank E, Trigg L, Hall M, Holmes G, Cunningham SJ. Weka: Practical machine learning tools and techniques with Java implementations. (Working paper 99/11). Hamilton, New Zealand: University of Waikato, Department of Computer Science, 1999. [30] Wu X, Kumar V, Quinlan JR, Ghosh J, Yang O, Motoda H, McLachlan GJ, Liu B, Yu PS, Zhou Z, Steinbach, M, Hand DJ, Steinberg D, 2007. “10 Algorithms in Data Mining”. Knowledge & Information Systems, Jan 2008, Vol. 14 Issue 1, p1-37, 37p, 4 Diagrams, 2 Graphs; DOI: 10.1007/s10115-007-0114-2. [31] Yu L, Liu H. “Feature Selection for High-Dimensional Data: A Fast Correlation-Based Filter Solution”. Department of Computer Science & Engineering, Arizona State University, Tempe, AZ 85287-5406, USA, 2003. [32] Zaki MJ, Wagner M. Data Mining and Analysis: Fundamental Concepts and Algorithms. Online (and Offline) Robust PCA, Novel, 2013.
Toplam 16 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Banu Abbasoğlu

Yayımlanma Tarihi 13 Temmuz 2020
Yayımlandığı Sayı Yıl 2020 Cilt: 3 Sayı: 1

Kaynak Göster

APA Abbasoğlu, B. (2020). Ortaokul Öğrencilerinin Akademik Başarılarının Eğitsel Veri Madenciliği Yöntemleri İle Tahmini. Veri Bilimi, 3(1), 1-10.



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