PERFORMANCE AND ACHIEVEMENT ANALYSIS OF A DATASET OF DISTANCE EDUCATION SAMPLES WITH WEKA

Yıl 2017, Cilt: 8 , 9 - 18, 09.12.2017

Tolga Demirhan İlker Hacioglu

Öz

Data mining methods can be used to create models that
will help in making meaningful deductions or even future predictions by
establishing relationships within records which have values that can not be
understood alone. In this study, a data set was created through the voluntary
participation of Trakya University, Tunca Vocational School (Distance
Education) students to a questionnaire. Weka, a data mining application, was
used to analyze the survey results. The most successful models on Weka for the
relevant data set and the attributes that affect student success were
investigated.

Anahtar Kelimeler

Weka, achievement analysis, data mining, distance education

Kaynakça

• Akçetin, E., & Çelik, U. (2015). İstenmeyen Elektronik Posta (Spam) Tespitinde Karar Ağacı Algoritmalarının Performans Kıyaslaması, İnternet Uygulamaları ve Yönetimi, (pp. 43-56). doi: 10.5505/iuyd.2014.43531 Araque, F., & Roldan, C., & Salguero, A. (2009). Factors influencing university drop out rate. Computer& Education, 53 (3), (pp. 563-574). https://doi.org/10.1016/j.compedu.2009.03.013 Aydın F. (2011). Kalp Ritim Bozukluğu Olan Hastaların Tedavi Süreçlerini Desteklemek Amaçlı Makine Öğrenmesine Dayalı Bir Sistemin Geliştirilmesi (Unpublished master dissertation). Trakya Üniversitesi Fen Bilimleri Enstitüsü, Edirne-Turkey. Bhawana, A., & Bharti, G. (2014). Review on Data Mining Techniques Used For Educational System, International Journal of Emerging Technology and Advanced Engineering Website: www.ijetae.com, 4 (11), Retrieved from http://www.ijetae.com/files/Volume4Issue11/IJETAE_1114_50.pdf Bulut, F. (2016). Performance Analysis of Ensemble Methods on Imbalanced Datasets. Bilişim Teknolojileri Dergisi, 9 (2), (pp. 153-159), doi: 10.17671/btd.81137 Chan, A.Y.K., & Chow, K.O., & Cheung, K.S. (2008) Online Course Refinement through Association Rule Mining. Journal of Educational Technology Systems, 36 (4). (pp. 433 – 44) Chandra,E., & Nandhini, K. (2010) Knowledge Mining from Student Data. European Journal of Scientific Research, ISSN 1450-216X 47 (1), (pp.156-163). Dener, M., & Dörterler, M., & Orman, A. (2009). Açık kaynak kodlu veri madenciliği programları: WEKA’da örnek uygulama, Akademik Bilisim’09 - XI. Akademik Bilişim Konferansı, Şanlıurfa. Erdoğan, S., & Timor, M. (2005). A Data Mining Application in a Student Database. Journal of Aeronautics and Space Technologies, 2(2), (pp.53-57). Guleria, P., & Sood, M. (2014). Data Mining In Education : A Review On The Knowledge Discovery Perspective. International Journal of Data Mining & Knowledge Management Process (IJDKP), 4(5), (pp.47-60) . J. Platt., (2000). Fast training of support vector machines using sequential minimal optimization. Advances in kernel methods: Support vector learning. Retrieved from https://www.microsoft.com/en-us/research/wp-content/uploads/2016/02/smo-book.pdf Longadge, R., & S. Dongre, & Malik, L., (2013). Class Imbalance Problem in Data Mining: Review International Journal of Computer Science and Network IJCSN , 2(1), ISSN (Online) : 2277-5420 Mishra, T., & Kumar, D., & Gupta, S., (2016). Students’ Employability Prediction Model through Data Mining. International Journal of Applied Engineering Research, 11 (4), ISSN 0973-4562, (pp:2275-2282). Noikajana, S., & Suwannasart, T., (2008). Web Service Test Case Generation Based on Decision Table, The Eighth International Conference on Quality Software, (pp:321-326), doi 10.1109/QSIC.2008.7: Öztemel, E. (2012). Yapay Sinir Ağları, Papatya Yayıncılık, Papatya & Kelebek Tasarım, ISBN: 978-975-6797-39-6, İstanbul. Pandeeswari, L., & Rajeswari, K. (2014). Student Academıc Performance Using Data Mining Techniques. International Journal of Computer Science and Mobile Computing, IJCSMC, 3 (10), (pp.726–731). Rajpal, R., & Kaur, S., & Kaur, R. (2016). Improving Detection Rate Using Misuse Detection and Machine Learning. SAI Computing Conference. London, UK. Syahela Hussien, N., & Sulaiman, S., & Mariyam Shamsuddin, S., (2016). Tools in Data Science for Better Processing. AIP Conference Proceedings, doi: 10.1063/1.4954530