Bu araştırmanın amacı, Ders
Yönetim Sistemi Yazılımı (DYS) kullanan öğrencilerin sistemle olan
etkileşimlerine dayalı olarak veri madenciliği sınıflandırma algoritmaları ile
öğrencilerin akademik başarılarının ne ölçüde tahmin edilebileceğini ve onların
akademik başarılarının tahmin edilmesinde algoritmalara hangi etkileşimlerin
bilgi kazancı sağladığını belirlemektir. Araştırma verileri, Trakya
Üniversitesi Eğitim Fakültesinde 2015-2016 eğitim öğretim yılında öğrenim
görmekte olan 70 öğrencinin Bulutders DYS yazılımını kullanma sürecindeki
etkileşimlerinden elde edilmiştir. Veri madenciliği sınıflandırma algoritmaları
olarak Naive Bayes, Karar Ağacı (C4.5) ve En Yakın k-Komşu algoritmaları
kullanılmıştır. Araştırma sonucunda elde edilen bulgulara göre, sınıflandırma
algoritmalarının doğruluk değerlerinin %55.7 ile %64.3 arasında olduğu ve DYS
yazılımındaki ödevlerden elde edilen öğrenci etkileşimlerinin en yüksek bilgi
kazancını sağladığı ortaya çıkmıştır.
Ahmad, F., Ismail, N. H. ve Aziz, A. A. (2015). The prediction of students’ academic performance using classification data mining techniques. Applied Mathematical Sciences, 9(129). https://doi.org/10.12988/ams.2015.53289
Akçapınar, G., Altun, A. ve Aşkar, P. (2015). Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment. İlköğretim Online, 14(2), 815–824. https://doi.org/10.17051/io.2015.03160
Akçapınar, G., Coşgun, E. ve Altun, A. (2013). Mining Wiki Usage Data for Predicting Final Grades of Students. Prague 2013 (IAC-ETeL 2013), 1–6.
Baldi, P., ve Brunak, S. (2001). Bioinformatics - The machine learning approach. Machine Learning.
Baradwaj, B. ve Pal, S. (2012). Mining educational data to analyze student’s performance. Internation Journal Od Advamced Computer Science and Applications, 2(6), 63–69. https://doi.org/vol.2,No.6
Bhardwaj, B. K. (2011). Data Mining : A prediction for performance improvement using classification. (IJCSIS) International Journal of Computer Science and Information Security, 9(4).
Breiman, L., Friedman, J. H., Olshen, R. A., ve Stone, C. J. (1984). Classification and Regression Trees. Wadsworth International Group. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Classification+and+regression+trees#0
Buniyamin, N., Mat, U. Bin ve Arshad, P. M. (2016). Educational data mining for prediction and classification of engineering students achievement. In IEEE 7th International Conference on Engineering Education, ICEED 2015. https://doi.org/10.1109/ICEED.2015.7451491
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. ve Zanasi, A. (1998). Discovering data mining : from concept to implementation. Prentice Hall.
Christian, T. M. ve Ayub, M. (2014). Exploration of classification using NBTree for predicting students’ performance. In Proceedings of 2014 International Conference on Data and Software Engineering, ICODSE 2014. https://doi.org/10.1109/ICODSE.2014.7062654
Del, A. (2016). Predicting academic performance in traditional environments at higher-education institutions using data mining : A review, (February 2017).
Fayyad, U. M., Weir, N. ve Djorgovski, S. G. (1993). Automated Cataloging And Analysis Of Sky Survey Image Databases: The Skicat System. Cikm, 527–536. https://doi.org/10.1145/170088.170414
Frank, E., Hall, M. A. ve Witten, I. H. (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques" Morgan Kaufmann, Fourth Edition.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. ve Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–4. https://doi.org/10.1038/nature07634
Güldal, H. (2015). Servis Olarak Bir Ders Yönetim Sistemi Yazılımı: Bulutders.com. In 5th International Conference on Research in Education – ICRE. Edirne.
Márquez-Vera, C., Cano, A., Romero, C. ve Ventura, S. (2013). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 38(3), 315–330. https://doi.org/10.1007/s10489-012-0374-8
Martins, L. L. ve Kellermanns, F. W. (2004). A Model of Business School Students’ Acceptance of a Web-Based Course Management System. Academy of Management Learning & Education, 3(1), 7–26. https://doi.org/10.5465/AMLE.2004.12436815
Mishra, T., Kumar, D. ve Gupta, S. (2014). Mining students’ data for prediction performance. International Conference on Advanced Computing and Communication Technologies, ACCT, 255–262. https://doi.org/10.1109/ACCT.2014.105
Osmanbegović, E. ve Suljić, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, X(1), 3–12.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1023/A:1022643204877
Quinlan, J. R. (1992). C4.5: Programs for Machine Learning. Morgan Kaufmann San Mateo California (Vol. 1). https://doi.org/10.1016/S0019-9958(62)90649-6
Shahiri, A. M., Husain, W. ve Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Tüzüntürk, S. (2010). Veri̇ Madenci̇li̇ği̇ ve İstati̇sti̇k, (2001), 65–90.
Year 2017,
Volume: 21 Issue: 4, 1355 - 1367, 20.12.2017
Ahmad, F., Ismail, N. H. ve Aziz, A. A. (2015). The prediction of students’ academic performance using classification data mining techniques. Applied Mathematical Sciences, 9(129). https://doi.org/10.12988/ams.2015.53289
Akçapınar, G., Altun, A. ve Aşkar, P. (2015). Modeling Students’ Academic Performance Based on Their Interactions with the Online Learning Environment. İlköğretim Online, 14(2), 815–824. https://doi.org/10.17051/io.2015.03160
Akçapınar, G., Coşgun, E. ve Altun, A. (2013). Mining Wiki Usage Data for Predicting Final Grades of Students. Prague 2013 (IAC-ETeL 2013), 1–6.
Baldi, P., ve Brunak, S. (2001). Bioinformatics - The machine learning approach. Machine Learning.
Baradwaj, B. ve Pal, S. (2012). Mining educational data to analyze student’s performance. Internation Journal Od Advamced Computer Science and Applications, 2(6), 63–69. https://doi.org/vol.2,No.6
Bhardwaj, B. K. (2011). Data Mining : A prediction for performance improvement using classification. (IJCSIS) International Journal of Computer Science and Information Security, 9(4).
Breiman, L., Friedman, J. H., Olshen, R. A., ve Stone, C. J. (1984). Classification and Regression Trees. Wadsworth International Group. Retrieved from http://scholar.google.com/scholar?hl=en&btnG=Search&q=intitle:Classification+and+regression+trees#0
Buniyamin, N., Mat, U. Bin ve Arshad, P. M. (2016). Educational data mining for prediction and classification of engineering students achievement. In IEEE 7th International Conference on Engineering Education, ICEED 2015. https://doi.org/10.1109/ICEED.2015.7451491
Cabena, P., Hadjinian, P., Stadler, R., Verhees, J. ve Zanasi, A. (1998). Discovering data mining : from concept to implementation. Prentice Hall.
Christian, T. M. ve Ayub, M. (2014). Exploration of classification using NBTree for predicting students’ performance. In Proceedings of 2014 International Conference on Data and Software Engineering, ICODSE 2014. https://doi.org/10.1109/ICODSE.2014.7062654
Del, A. (2016). Predicting academic performance in traditional environments at higher-education institutions using data mining : A review, (February 2017).
Fayyad, U. M., Weir, N. ve Djorgovski, S. G. (1993). Automated Cataloging And Analysis Of Sky Survey Image Databases: The Skicat System. Cikm, 527–536. https://doi.org/10.1145/170088.170414
Frank, E., Hall, M. A. ve Witten, I. H. (2016). The WEKA Workbench. Online Appendix for "Data Mining: Practical Machine Learning Tools and Techniques" Morgan Kaufmann, Fourth Edition.
Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S. ve Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–4. https://doi.org/10.1038/nature07634
Güldal, H. (2015). Servis Olarak Bir Ders Yönetim Sistemi Yazılımı: Bulutders.com. In 5th International Conference on Research in Education – ICRE. Edirne.
Márquez-Vera, C., Cano, A., Romero, C. ve Ventura, S. (2013). Predicting student failure at school using genetic programming and different data mining approaches with high dimensional and imbalanced data. Applied Intelligence, 38(3), 315–330. https://doi.org/10.1007/s10489-012-0374-8
Martins, L. L. ve Kellermanns, F. W. (2004). A Model of Business School Students’ Acceptance of a Web-Based Course Management System. Academy of Management Learning & Education, 3(1), 7–26. https://doi.org/10.5465/AMLE.2004.12436815
Mishra, T., Kumar, D. ve Gupta, S. (2014). Mining students’ data for prediction performance. International Conference on Advanced Computing and Communication Technologies, ACCT, 255–262. https://doi.org/10.1109/ACCT.2014.105
Osmanbegović, E. ve Suljić, M. (2012). Data mining approach for predicting student performance. Journal of Economics and Business, X(1), 3–12.
Quinlan, J. R. (1986). Induction of Decision Trees. Machine Learning, 1(1), 81–106. https://doi.org/10.1023/A:1022643204877
Quinlan, J. R. (1992). C4.5: Programs for Machine Learning. Morgan Kaufmann San Mateo California (Vol. 1). https://doi.org/10.1016/S0019-9958(62)90649-6
Shahiri, A. M., Husain, W. ve Rashid, N. A. (2015). A Review on Predicting Student’s Performance Using Data Mining Techniques. Procedia Computer Science, 72, 414–422. https://doi.org/10.1016/j.procs.2015.12.157
Tüzüntürk, S. (2010). Veri̇ Madenci̇li̇ği̇ ve İstati̇sti̇k, (2001), 65–90.
Güldal, H., & Çakıcı, Y. (2017). Ders Yönetim Sistemi Yazılımı Kullanıcı Etkileşimlerinin Sınıflandırma Algoritmaları ile Analizi. Atatürk Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 21(4), 1355-1367.