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

Yıl 2018, Cilt: 8 Sayı: 2, 108 - 124, 15.07.2018

Ahmet Tekin , Zeynep Öztekin

https://doi.org/10.17943/etku.351473

Cited By: 3

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.

Anahtar Kelimeler

eğitsel veri madenciliği; veri madenciliği; derleme çalışması

Kaynakça

• Akpınar, H. (2000). Veri tabanlarında bilgi keşfi ve veri madenciliği. İÜ İşletme Fakültesi Dergisi, 29(1), 1-22.
• Arslan, H. Sakarya Üniversitesi Web Sitesi Erişim Kayıtlarının Web Madenciliği ile Analizi, (Sakarya Üniversitesi Fen Bilimler Enstitüsü Yüksek Lisans Tezi), 2008.
• Aydın, Y. D. D. S., & Özkul, A. E (2015). Veri Madenciliği Ve Anadolu Üniversitesi Açıköğretim Sisteminde Bir Uygulama.
• Baran, E. (2013). Öğretim teknolojilerinde yeni eğilimler ve yaklaşımlar. K. Çağıltay & Y. Göktaş. Öğretim Teknolojilerinin Temelleri: Teoriler, Araştırmalar, Eğilimler, 34, 567-581.
• Bozkır, A. S. OLAP ve Veri Madenciliği Teknolojilerinden Yararlanılarak Web Tabanlı Bir Karar Destek Sisteminin Gerçekleştirilmesi, (Hacettepe Üniversitesi Fen Bilimler Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2009.
• Gürcan, F. Web İçerik Madenciliği ve Konu Sınıflandırması, (Karadeniz Teknik Üniversitesi Fen Bilimler Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2009.
• Gürsoy, Tuğba Şimşek (2009). Gürsoy, U. T. Ş. (2009). Veri madenciliği ve Bilgi Keşfi. Pegem Akademi.
• Kaya, H. ve Köymen, K. (2008). “Veri Madenciliği Kavramı ve Uygulama Alanları”, Doğu Anadolu Bölgesi Araştırmaları, 159-164.
• Kayri, M. (2008). Elektronik portfolyo değerlendirmeleri için veri madenciliği yaklaşımı. Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 5(1), 98-110.
• Kayri, M., & Boysan, M. (2008). Assesment of relation between cognitive vulnerability and depression's level by using classification and regression tree analysis. Hacettepe Universitesi Egitim Fakultesi Dergisi-Hacettepe University Journal of Education, (34), 168-177.
• Kılınç, Çağrı. Üniversite Öğrenci Başarısı Üzerine Etki Eden Faktörlerin Veri Madenciliği Yöntemleri İle İncelenmesi, (Eskişehir Osmangazi Üniversitesi Fen Bilimleri Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2015.
• Laudon, K. C., & Laudon, J. P. (2011). Yönetim Bilişim Sistemleri Dijital İşletmeyi Yönetme, Çev. Ed. Yozgat U., Nobel Yayıncılık. Oracle Corporation and/or its affiliates,(2013).
• Özbay, Özkan. Öğretim Yönetim Sistemi Üzerinde Üniversite (Lisans) Düzeyindeki Öğrenci Hareketliliğinin Veri Madenciliği Yöntemleriyle Analizi, (Başkent Üniversitesi Eğitim Bilimleri Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2015.
• Özçakır, F. C., & Çamurcu, A. Y. (2007). Birliktelik Kuralı Yöntemi İçin Bir Veri Madenciliği Yazılımı Tasarımı Ve Uygulaması.
• Peña-Ayala, A. (2014). Educational data mining: A survey and a data mining-based analysis of recent works. Expert systems with applications,41(4), 1432-1462.
• Romero, C., & Ventura, S. (2007). Educational data mining: A survey from 1995 to 2005. Expert systems with applications, 33(1), 135-146.
• Romero, C., Ventura, S., Pechenizkiy, M., & Baker, R. S. (Eds.). (2010).Handbook of educational data mining. CRC Press.
• Ünlükahraman, Orhan. Web Tabanlı Eğitimde Web Madenciliği Uygulaması İle Öğrenci Davranışlarının Analizi, (Fırat Üniversitesi Eğitim Bilimleri Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2011.
• 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.
• Yurdakul, Semra. Veri Madenciliği ile Lise Öğrenci Performanslarının Değerlendirilmesi, (Kırıkkale Üniversitesi Fen Bilimleri Enstitüsü Yayınlanmamış Yüksek Lisans Tezi), 2015.
• Zimmermann, J., Brodersen, K. H., Heinimann, H. R., & Buhmann, J. M. (2015). A model-based approach to predicting graduate-level performance using indicators of undergraduate-level performance. JEDM-Journal of Educational Data Mining, 7(3), 151-176.
• İncelenen Kaynaklar
• Abdullah, Z., Herawan, T., Ahmad, N., & Deris, M. M. (2011). Mining significant association rules from educational data using critical relative support approach. Procedia-Social and Behavioral Sciences, 28, 97-101.
• Aher, S. B., & Lobo, L. M. R. J. (2011). Data mining in educational system using Weka. In IJCA Proceedings on International Conference on Emerging Technology Trends (ICETT) (Vol. 3, pp. 20-25).
• Ahmed, A. M., Rizaner, A., ve Ulusoy, A. H. (2016). Using data Mining to Predict Instructor Performance. Procedia Computer Science, 102, 137-142.
• Ahmed, A. B. E. D., & Elaraby, I. S. (2014). Data Mining: A prediction for Student's Performance Using Classification Method. World Journal of Computer Application and Technology, 2(2), 43-47.
• Albayrak, A. S., ve Yilmaz, S. K. (2009). Veri Madenciliği: Karar Ağaci Algoritmalari Ve İmkb Verileri Üzerine Bir Uygulama. Suleyman Demirel University Journal Of Faculty Of Economics & Administrative Sciences, 14(1).
• Algur, S. P., Bhat, P., & Kulkarni, N. (2016). Educational Data Mining: Classification Techniques for Recruitment Analysis. International Journal of Modern Education and Computer Science, 8(2), 59.
• Al-Razgan, M., Al-Khalifa, A. S., & Al-Khalifa, H. S. (2014). Educational data mining: A systematic review of the published literature 2006-2013. In Proceedings of the First International Conference on Advanced Data and Information Engineering (DaEng-2013) (pp. 711-719). Springer Singapore.
• AlShammari, I., Aldhafiri, M., & Al-Shammari, Z. (2013). A meta-analysis of educational data mining on improvements in learning outcomes. College Student Journal, 47(2), 326-333.
• Anaya, A. R., & Boticario, J. G. (2009, July). A data mining approach to reveal representative collaboration indicators in open collaboration frameworks. InEducational Data Mining 2009.
• Angeli, C., & Valanides, N. (2013). Using educational data mining methods to assess field-dependent and field-independent learners’ complex problem solving. Educational Technology Research and Development, 61(3), 521-548.
• Anil, P. R. K. (2013). Predicting Course and Branch Interest among Higher Education Students from Rural and Semi-Urban Area using Data Mining Techniques. International Journal of Advanced Research in Computer Science, 4(10).
• Anjewierden, A., Kolloffel, B., & Hulshof, C. (2007). Towards educational data mining: Using data mining methods for automated chat analysis to understand and support inquiry learning processes. In International Workshop on Applying Data Mining in e-Learning (ADML 2007).
• Antonenko, P. D., Toy, S., & Niederhauser, D. S. (2012). Using cluster analysis for data mining in educational technology research. Educational Technology Research and Development, 60(3), 383-398.
• Appalla, P., Kuthadi, V. M., & Marwala, T. (2016). An efficient educational data mining approach to support e-learning. In Information Systems Design and Intelligent Applications (pp. 63-75). Springer India.
• Auddy, A., & Mukhopadhyay, S. (2014). Studies on ICT Usage in the Academic CampusUsing Educational Data Mining. International Journal of Modern Education and Computer Science, 6(6), 10.
• Aydın, S. (2007). Veri madenciliği ve Anadolu üniversitesi uzaktan eğitim sisteminde bir uygulama. Doktora Tezi, Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü.
• Baker, R. S., & Yacef, K. (2009). The state of educational data mining in 2009: A review and future visions. JEDM-Journal of Educational Data Mining,1(1), 3-17.
• Bannert, M., Reimann, P., & Sonnenberg, C. (2014). Process mining techniques for analysing patterns and strategies in students’ self-regulated learning. Metacognition and learning, 9(2), 161-185.
• Bâra, A., Andreescu, A., Botha, I., Florea, A., & Velicanu, M. (2013, May). DATA MINING SOLUTIONS FOR DETERMINING STUDENT'S PROFILE. In The International Scientific Conference eLearning and Software for Education(Vol. 2, p. 284). " Carol I" National Defence University.
• Baradwaj, B. K., & Pal, S. (2012). Mining educational data to analyze students' performance. arXiv preprint arXiv:1201.3417.
• Baepler, P., & Murdoch, C. J. (2010). Academic analytics and data mining in higher education. International Journal for the Scholarship of Teaching and Learning, 4(2), 17.
• Berland, M., Baker, R. S., & Blikstein, P. (2014). Educational data mining and learning analytics: Applications to constructionist research. Technology, Knowledge and Learning, 19(1-2), 205-220.
• Bilen, Ö., Hotaman, D., Aşkın, Ö. E., ve Büyüklü, A. H. (2014). LYS Başarılarına Göre Okul Performanslarının Eğitsel Veri Madenciliği Teknikleriyle İncelenmesi: 2011 İstanbul Örneği. Eğitim Ve Bilim, 39(172).
• Bouarab-Dahmani, F., & Tahi, R. (2013, January). Automated evaluation results analysis with data mining algorithms. In ECEL2013-Proceedings for the 12th European Conference on eLearning: ECEL 2013 (p. 41). Academic Conferences Limited.
• Bousbia, N., & Belamri, I. (2014). Which Contribution Does EDM Provide to Computer-Based Learning Environments?. In Educational data mining (pp. 3-28). Springer International Publishing.
• Bushatı, S., Nınka, I., & Kalemı, E. (2013). Data Mining And Virtual Classes, İnnovation İn The Educational System. Scıence, Innovatıon New Technology, 41.
• Calders, T., & Pechenizkiy, M. (2012). Introduction to the special section on educational data mining. ACM SIGKDD Explorations Newsletter, 13(2), 3-6.
• Campo-Ávila, J. D., Conejo, R., Triguero, F., & Morales-Bueno, R. (2015). Mining Web-based Educational Systems to Predict Student Learning Achievements. International Journal of Interactive Multimedia & Artificial Intelligence, 3(2).
• Çağdaş, Kurt, ve Erdem, O. A. (2012). Öğrenci Başarısını Etkileyen Faktörlerin Veri Madenciliği Yöntemleriyle İncelenmesi. Politeknik Dergisi,15(2).
• Chalaris, M., Gritzalis, S., Maragoudakis, M., Sgouropoulou, C., & Lykeridou, K. (2015, February). Examining Students' Graduation İssues Using Data Mining Techniques-The Case Of TEI Of Athens. In INTERNATIONAL CONFERENCE ON INTEGRATED INFORMATION (IC-ININFO 2014): Proceedings Of The 4th International Conference On Integrated Information(Vol. 1644, No. 1, Pp. 255-262). AIP Publishing.
• Chaware, A. N. (2011). Educational data mining: An emerging trends in Education. International Journal of Advanced Research in Computer Science, 2(6).
• Chaware, A. N., & Lanjewar, U. A. (2014). A Novel Educational Datamining Model to Attain Sustainability. International Journal of Advanced Research in Computer Science, 5(1).
• Cho, M. H., & Yoo, J. S. (2016). Exploring online students’ self-regulated learning with self-reported surveys and log files: a data mining approach. Interactive Learning Environments, 1-13.
• Defreitas, K., & Bernard, M. (2015). Comparatıve Performance Analysıs Of Clusterıng Technıques In Educatıonal Data Mınıng.Iadıs International Journal On Computer Science & Information Systems,10(2).
• Dogan, B., & Camurcu, A. Y. (2010). Visual clustering of multidimensional educational data from an intelligent tutoring system. Computer Applications in Engineering Education, 18(2), 375-382.
• Durairaj, M., & Vijitha, C. (2014). Educational Data mining for Prediction of Student Performance Using Clustering Algorithms. International Journal of Computer Science and Information Technologies, 5(4), 5987-5991.
• Durango-Cohen, E. J., & Balasubramanian, S. K. (2015). Effective Segmentation of University Alumni: Mining Contribution Data with Finite-Mixture Models. Research in Higher Education, 56(1), 78-104.
• El-Halees, A. (2009). Mining students data to analyze e-Learning behavior: A Case Study. Department of Computer Science, Islamic University of Gaza PO Box, 108.
• Fırat, A. P. D. M., ve Yuzer, T. V. (2016). Learnıng Analytıcs: Assessment Of Mass Data In Dıstance Educatıon. International Journal On New Trends İn Education & Their Implications (Ijonte), 7(2).
• Fırat, M. (2015). Eğitim Teknolojileri Araştırmalarında Yeni Bir Alan: Öğrenme Analitikleri. Mersin Üniversitesi Eğitim Fakültesi Dergisi, 11(3).
• Fu, J., Zapata, D., & Mavronikolas, E. (2014). Statistical Methods for Assessments in Simulations and Serious Games. ETS Research Report Series, 2014(2), 1-17. García-Saiz, D., Palazuelos, C., & Zorrilla, M. (2014). Data mining and social network analysis in the educational field: An application for non-expert users. In Educational Data Mining (pp. 411-439). Springer International Publishing.
• Gates, K., Wilkins, D., Conlon, S., Mossing, S., & Eftink, M. (2014). Maximizing the value of student ratings through data mining. In Educational Data Mining (pp. 379-410). Springer International Publishing.
• Gašević, D., Dawson, S., & Siemens, G. (2015). Let’s not forget: Learning analytics are about learning. TechTrends, 59(1), 64-71.
• Gobert, J. D., Kim, Y. J., Sao Pedro, M. A., Kennedy, M., & Betts, C. G. (2015). Using educational data mining to assess students’ skills at designing and conducting experiments within a complex systems microworld. Thinking Skills and Creativity, 18, 81-90.
• Gobert, J. D., Sao Pedro, M. A., Baker, R. S., Toto, E., & Montalvo, O. (2012). Leveraging educational data mining for real-time performance assessment of scientific inquiry skills within microworlds. JEDM-Journal of Educational Data Mining, 4(1), 111-143.
• Gobert, J. D., Sao Pedro, M., Raziuddin, J., & Baker, R. S. (2013). From log files to assessment metrics: Measuring students' science inquiry skills using educational data mining. Journal of the Learning Sciences, 22(4), 521-563.
• Guo, N. Y. (2013). ReCa: A Social Relationship Mining Tool in Virtual Learning Environment. International Journal of Emerging Technologies in Learning, 8(3). Hung, J. L., & Zhang, K. (2008). Revealing online learning behaviors and activity patterns and making predictions with data mining techniques in online teaching. MERLOT Journal of Online Learning and Teaching.
• Idil, F. H., Narli, S., & Aksoy, E. (2016). Using data mining techniques examination of the middle school students’ attitude towards mathematics in the context of some variables. International Journal of Education in Mathematics, Science and Technology, 4(3), 210-228.
• Ivančević, V., Knežević, M., Pušić, B., & Luković, I. (2014). Adaptive testing in programming courses based on educational data mining techniques. In Educational data mining (pp. 257-287). Springer International Publishing.
• Jiang, Y. H., Javaad, S. S., & Golab, L. (2016). Data Mining of Undergraduate Course Evaluations. Informatics in Education-An International Journal, (Vol15_1), 85-102.
• Jindal, R., & Borah, M. D. (2013). A survey on educational data mining and research trends. International Journal of Database Management Systems, 5(3), 53.
• Johnson, D., & Samora, D. (2016). The Potential Transformation Of Higher Education Through Computer-Based Adaptive Learning Systems. Global Education Journal, 2016(1).
• Jugo, I., Kovačić, B., & Slavuj, V. (2016). Increasing the Adaptivity of an Intelligent Tutoring System with Educational Data Mining: A System Overview. International Journal of Emerging Technologies in Learning,11(3).
• Kabra, R. R., & Bichkar, R. S. (2011). Performance prediction of engineering students using decision trees. International Journal of Computer Applications, 36(11), 8-12.
• Kagklis, V., Karatrantou, A., Tantoula, M., Panagiotakopoulos, C. T., & Verykios, V. S. (2015). A Learning Analytics Methodology for Detecting Sentiment in Student Fora: A Case Study in Distance Education. European Journal of Open, Distance and E-learning, 18(2), 74-94.
• Kayri, M. (2015). An Intelligent Approach to Educational Data: Performance Comparison of the Multilayer Perceptron and the Radial Basis Function Artificial Neural Networks. Educational Sciences: Theory & Practice, 1, 1-10.
• Kım, J. (2016). Who İs Teaching Data: Meeting The Demand For Data Professionals. Journal Of Education For Library And Information Science.
• Karabatak, M. (2012). Investigation of the Effect of Social Networks on Students by Using Data Mining. Education Sciences, 7(1), 155-164.
• Kotsiantis, S. (2009). Educational data mining: a case study for predicting dropout-prone students. International Journal of Knowledge Engineering and Soft Data Paradigms, 1(2), 101-111.
• Kotsiantis, S. B. (2012). Use of machine learning techniques for educational proposes: a decision support system for forecasting students’ grades. Artificial Intelligence Review, 37(4), 331-344.
• Krüger, A., Merceron, A., & Wolf, B. (2010, June). A data model to ease analysis and mining of educational data. In Educational Data Mining 2010.
• Kumar, M., Shambhu, S., & Aggarwal, P. (2016). Recognition of Slow Learners Using Classification Data Mining Techniques. Imperial Journal of Interdisciplinary Research, 2(12).
• Kumar, V., & Chadha, A. (2011). An empirical study of the applications of data mining techniques in higher education. International Journal of Advanced Computer Science and Applications, 2(3).
• Kumar, S. A., & Vijayalakshmi, M. N. (2011, October). Efficiency of decision trees in predicting student’s academic performance. In First International Conference on Computer Science, Engineering and Applications, CS and IT (Vol. 2, pp. 335-343).
• Laudon, K. C., & Laudon, J. P. (2011). Yönetim Bilişim Sistemleri. Çeviri Editörü: Prof. Dr. Uğur Yozgat) Ankara: Nobel Akademik Yayıncılık. Lin, S. P. (2015). Using EDM for Developing EWS to Predict University Students Drop Out. International Journal of Intelligent Technologies and Applied Statistics, 8(4), 339-362.
• Liñán, L. C., & Pérez, Á. A. J. (2015). Educational Data Mining and Learning Analytics: differences, similarities, and time evolution. Revista de Universidad y Sociedad del Conocimiento, 12(3), 98-112.
• Lopez, M. I., Luna, J. M., Romero, C., & Ventura, S. (2012). Classification via Clustering for Predicting Final Marks Based on Student Participation in Forums. International Educational Data Mining Society.
• Lustigova, Z., & Brom, P. (2014). Educational Datamining in Virtual Learning Environments. International Journal of Advanced Corporate Learning, 7(1).
• Machado, C. J., Lima, B. R., Maciel, A. M., & Rodrigues, R. L. (2016). An investigation of students’ behavior in discussion forums using Educational Data Mining. In The 28 th International Conference on Software Engineering & Knowledge Engineering SEKE-2016. Califorina-USA: Jul.
• Magdin, M., & Turcáni, M. (2015). Personalization of Student in Course Management Systems on the Basis Using Method of Data Mining. Turkish Online Journal of Educational Technology-TOJET, 14(1), 58-67.
• Mahesh, J. U., Chandrakanth, N., & Reddy, M. R. (2016). Data Analytics in Abroad and Indian Education System-Using Data Mining Classification Techniques by R Language. Journal of Data Mining and Management, 1(2).
• Marquez-Vera, C., Romero, C., & Ventura, S. (2010, June). Predicting school failure using data mining. In Educational Data Mining 2011.
• Márquez‐Vera, C., Cano, A., Romero, C., Noaman, A. Y. M., Mousa Fardoun, H., & Ventura, S. (2016). Early dropout prediction using data mining: a case study with high school students. Expert Systems, 33(1), 107-124.
• Markauskaite, L., & Reimann, P. (2014). Editorial: e‐Research for education: Applied, methodological and critical perspectives. British Journal of Educational Technology, 45(3), 385-391.
• Martínez Abad, F., & Chaparro Caso López, A. A. (2016). Data-mining techniques in detecting factors linked to academic achievement. School Effectiveness and School Improvement, 1-17.
• Mislevy, R. J., Behrens, J. T., Dicerbo, K. E., & Levy, R. (2012). Design and discovery in educational assessment: Evidence-centered design, psychometrics, and educational data mining. JEDM-Journal of Educational Data Mining, 4(1), 11-48.
• Moroney, K. M., & Makh, S. U. (2012). Data mining Application to Design a System for Performance Improvisation of Students in Their Academic Studies.
• Mueen, A., Zafar, B., & Manzoor, U. (2016). Modeling and Predicting Students’ Academic Performance Using Data Mining Techniques. International Journal of Modern Education & Computer Science, 8(11).
• Mundada, O. (2016). Mining Educational Data From Student's Management System. International Journal of Advanced Research in Computer Science, 7(3).
• Nakhkob, B., & Khademi, M. (2016). Predicted Increase Enrollment in Higher Education Using Neural Networks and Data Mining Techniques. Journal of Advances in Computer Research, 7(4), 125-140.
• Natek, S., & Zwilling, M. (2014). Student data mining solution–knowledge management system related to higher education institutions. Expert systems with applications, 41(14), 6400-6407.
• 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).
• Singh, S., & Kumar, V. (2013). Performance Analysis of Engineering Students for Recruitment Using Classification Data Mining Techniques.International Journal of Science, Engineering and Computer Technology,3(2), 31.
• 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.
• Şuşnea, E. (2013). Using data mining in e-learning-A generic framework for military education. In Conference proceedings of» eLearning and Software for Education «(eLSE) (No. 01, pp. 411-415). Universitatea Nationala de Aparare Carol I.
• Tair, M. M. A., & El-Halees, A. M. (2012). Mining educational data to improve students' performance: a case study. International Journal of Information, 2(2).
• Tama, B. A. (2015). Learning to Prevent Inactive Student of Indonesia Open University. Journal of Information Processing Systems, 11(2), 165-172.
• 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.