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A Systematic Analysis on Learning Outcomes in Researches Using Data Mining Method in Distance Education

Year 2022, , 197 - 226, 28.12.2022
https://doi.org/10.53694/bited.1131475

Abstract

The data obtained as a result of the age we live in and the constantly evolving technology is increasing day by day. With these data, it is possible to obtain the fastest, most meaningful and more accurately predictable decisions through data mining. To put it briefly, data mining is defined as the act of processing raw information obtained into data, processing that data like an artist and transforming it into a work. Data mining is of great importance for many fields. Data mining has a wide scope of usage in numerous industries, including health, technology and education. Within the scope, educational data mining is the sub-title. Traditional and distant education studies are the focus of educational data mining. The aim of this study is to determine the effect of the results obtained by using data mining in distance education on the students’ learning outcomes as a result of the trends in related research. In the research, studies accessed from the Web of Science database were systematically reviewed within the scope of the concepts of distance education and data mining. In this context, this study will provide an analysis of the studies within the literature and therefore, the effect of distance education on learning outcomes as seen by data mining. In addition, it is thought that determining the studies needed in the research area as a result of systematic review will be a guide for researchers.

References

  • Akcapinar, G., & Bayazit, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. Turkish Online Journal of Distance Education, 19(4), 116-125.
  • Akçapınar, G. (2014). Çevrimiçi öğrenme ortamındaki etkileşim verilerine göre öğrencilerin akademik performanslarının veri madenciliği yaklaşımı ile modellenmesi. (Yayımlanmamış Doktora Tezi). Hacettepe Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Akmeşe, Ö. F., Kör, H., & Erbay, H. (2021). Use of machine learning techniques the forecast of student achievement in higher aducation. Information Technologies and Learning Tools, 82(2), 297-311.
  • Akyürek, M. İ. (2020). Uzaktan Eğitim: Bir Alanyazın taraması. Medeniyet Eğitim Araştırmaları Dergisi, 4(1), 1-9. Retrieved from https://dergipark.org.tr/tr/pub/mead/issue/56310/711904
  • Al-Musharraf, A., & Alkhattabi, M. (2016). An educational data mining approach to explore the effect of using interactive supporting features in an LMS for overall performance within an online learning environment. International Journal of Computer Science and Network Security (IJCSNS), 16(3), 1.
  • Anaya, A. R., Luque, M., & Peinado, M. (2016). A visual recommender tool in a collaborative learning experience. Expert Systems with Applications, 45, 248-259.
  • Andrade, T. L. D., Rigo, S. J., & Barbosa, J. L. V. (2021). Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study. Informatics in Education, 20(2).
  • Aydın, S., (2007). Veri madenciliği ve Anadolu Üniversitesi uzaktan eğitim sisteminde bir uygulama. (Yayımlanmamış Doktora Tezi). Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir.
  • Aydoğdu, Ş. (2020). Educational data mining studies in Turkey: A systematic review. Turkish Online Journal of Distance Education, 21(3), 170-185.
  • Bezerra, L. N. M., & Silva, M. T. (2020). Educational Data Mining Applied to a Massive Course. International Journal of Distance Education Technologies, 18(4), 17–30. doi:10.4018/ijdet.2020100102
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
  • Bouchet, F., Harley, J. M., Trevors, G. J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104-146. https://doi.org/10.5281/zenodo.3554613
  • Bozkurt, A. (2017). Türkiye’de uzaktan eğitimin dünü, bugünü ve yarını. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 3(2), 85-124.
  • Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5), 318-331. doi: 10.1504/IJTEL.2012.051815
  • Chen, H., Dai, Y., Gao, H., Han, D., & Li, S. (2019). Classification and analysis of moocs learner’s state: The study of hidden markov model. Computer Science and Information Systems, 16(3), 849-865.
  • Cheng, L. C., Chu, H. C., & Shiue, B. M. (2015). An innovative approach for assisting teachers in improving instructional strategies via analyzing historical assessment data of students. International Journal of Distance Education Technologies (IJDET), 13(4), 40-61.
  • Cihan, P. (2018). Veri madenciliği yöntemleriyle hayvan hastalıklarında teşhis, prognoz ve risk faktörlerinin belirlenmesi. (Yayımlanmamış Doktora Tezi). Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Coşlu, E. (2013). Veri madenciliği. Akademik bilişim, 23-25.
  • Çelebi, V. (2019). Bayes teoremi̇ bağlamında olasılıkçı bayes epi̇stemoloji̇si̇ni̇n kapsamı üzeri̇ne bi̇r ı̇nceleme. FLSF Felsefe ve Sosyal Bilimler Dergisi (28):319–43.
  • Çiltaş, A. (2011). Eğitimde öz-düzenleme öğretiminin önemi üzerine bir çalışma. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(5), 1-11.
  • Demirel, M. (1993). Öğrenme stratejilerinin öğretimi. Eğitim ve Bilim, 17(88).
  • Dinçer, S. (2016). Bilgisayar Destekli Eğitim ve Uzaktan Eğitime Genel Bir Bakış. Adana, Seyhan, Türkiye.
  • Erfidan, Ali. (2019). Derslerin uzaktan eğitim yoluyla verilmesiyle ilgili öğretim elemanı ve öğrenci görüşleri Balıkesir Üniversitesi örneği. (Yayınlanmamış Yüksek Lisans Tezi). Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, Balıkesir.
  • Erten, H. (2015). Veri Madenciliği Teknikleri ile Organ Nakli İçin Uygun Donör Oranının Hesaplanması. (Yayınlanmamış Yüksek lisans tezi). Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Fok, W. W., Chen, H., Yi, J., Li, S., Yeung, H. A., Ying, W., & Fang, L. (2014). Data mining application of decision trees for student profiling at the Open University of China. In 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 732-738). IEEE.
  • Gao, Y., & Zhang, S. (2018). Design of and research on autonomous learning system for distance education based on data mining technology. Educational Sciences: Theory & Practice, 18(6).
  • García, E., Romero, C., Ventura, S., & De Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88.
  • Hämäläinen, W., & Vinni, M. (2010). Classifiers for educational technology. Handbook on educational data mining.
  • Hämäläinen, W., & Vinni, M. (2011). Classifiers for educational data mining. Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 57-71.
  • Hampel, R., & Pleines, C. (2013). Fostering student interaction and engagement in a virtual learning environment: An investigation into activity design and implementation. Calico Journal, 30(3), 342-370.
  • Johnson, L., Smith, R., Willis, H., Levine, A. & Haywood, K. (2011). The 2011 horizon report, Austin, TX: The New Media Consortium.
  • Karaçam, Z. (2013). Sistematik derleme metodolojisi: Sistematik derleme hazırlamak için bir rehber. Dokuz Eylül Üniversitesi Hemşirelik Yüksekokulu Elektronik Dergisi, 6(1), 26-33.
  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Joint technical report Software Engineering Group, Keele University, United Kingdom and Empirical Software Engineering, National ICT Australia Ltd, Australia.
  • Koldere Akın, Y. (2008). Veri madenciliğinde kümeleme algoritmaları ve kümeleme analizi. (Yayınlanmamış Doktora Tezi). Marmara Üniversitesi, Sosyal Bilimler Üniversitesi, İstanbul.
  • Kumtepe, A. T., Atasoy, E., Kaya, Ö., Uğur, S., Dinçer, G. D., Erdoğdu, E., & Aydın, C. H. (2019). An Interaction Framework for Open and Distance Learning: Learning Outcomes, Motivation, Satisfaction, Perception. AJIT-e: Bilişim Teknolojileri Online Dergisi, 10(36), 7-26.
  • 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.
  • Maher, A. (2004). Learning outcomes in higher education: Implications for curriculum design and student learning. Journal of Hospitality, Leisure, Sport and Tourism Education, 3(2), 46-54.
  • Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.
  • Mullen, G. E., & Tallent-Runnels, M. K. (2006). Student outcomes and perceptions of instructors’ demands and support in online and traditional classrooms. The Internet and Higher Education, 9, 257-266.
  • Newman, M. & Gough, D. (2020). Systematic reviews in educational research: methodology, perspectives and application. In O. Zawacki-Richter, M. Kerres, S. Bedenlier, M. Bond, K. & Buntins (Eds.), Systematic reviews in educational research: Methodology, perspectives and application (pp. 3-22). Wiesbaden: Springer VS.
  • Özbay, Ö. (2015). Veri madenciliği kavramı ve eğitimde veri madenciliği uygulamaları. Uluslararası Eğitim Bilimleri Dergisi, (5), 262-272.
  • Özkan, Y. (2016). Veri Madenciliği Yöntemleri (4.Baskı), Ankara: Papatya Yayınları.
  • Preidys, S., & Sakalauskas, L. (2010). Analysis of students’ study activities in virtual learning environments using data mining methods. Technological and economic development of economy, 16(1), 94-108.
  • Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
  • Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. doi:10.1002/widm.1075
  • Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2010). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135–146. doi:10.1002/cae.20456
  • Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2013). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135-146. doi: 10.1002/cae.20456
  • Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
  • Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. doi: http://dx.doi.org/10.1016/j.compedu.2007.05.016
  • Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2008). Data mining algorithms to classify students. In Educational data mining 2008.
  • Sen, B., & Ucar, E. (2012). Evaluating the achievements of computer engineering department of distance education students with data mining methods. Procedia Technology, 1, 262-267.
  • Siemens, G., & Baker, R. S. D. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254).
  • Tekin, A. (2018). Tıp'ta veri madenciliği uygulamaları: Yenidoğan sepsisi veri seti analizi/Data mining applications in medicine: Newborn sepsis data set analysis.
  • Trigwell, K., & Prosser, M. (1991). Improving the quality of student learning: the influence of learning context and student approaches to learning on learning outcomes. Higher Education, 22(3), 251–266. doi:10.1007/bf00132290
  • Uzun, Y., Uzun, F. N., & Çakar, E. (2021). Veri madenciliği ve kullanım alanları. Uluslararası Mühendislik, Doğa ve Sosyal Bilimler Sempozyumu, Batman.
  • Viloria, A., López, J. R., Payares, K., Vargas-Mercado, C., Duran, S. E., Hernández-Palma, H., & David, M. A. (2019). Determinating Student Interactions in a Virtual Learning Environment Using Data Mining. Procedia Computer Science, 155, 587–592. doi:10.1016/j.procs.2019.08.082
  • Yasmin, D. (2013). Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Distance Education, 34(2), 218-231.
  • Yılmaz, K. (2021). Sosyal bilimlerde ve eğitim bilimlerinde sistematik derleme, meta değerlendirme ve bibliyometrik analizler. Manas Sosyal Araştırmalar Dergisi, 10(2), 1457-1490.
  • Yilmaz, R. (2017). Problems experienced in evaluating success and performance in distance education: A case study. Turkish Online Journal of Distance Education, 18(1), 39-51.
  • Yurdugül, H.& Menzi Çetin, N. (2015). Investigation of the relationship between learning process and learning outcomes in e-learning environments. Eurasian Journal of Educational Research, 59, 57-74. http://dx.doi.org/10.14689/ejer.2015.59.4
  • Yurtoğlu, H. (2005). Yapay Sinir Ağları Modellemesi ile Öngörü Modellemesi: Bazı Makroekonomik Değişkenler için Türkiye Örneği. (Uzmanlık Tezi). DPT, Ankara.
  • Zang, W., & Lin, F. (2003, August). Investigation of web-based teaching and learning by boosting algorithms. In International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003. (pp. 445-449). IEEE.
  • Zhang, X., Gao, Y., Yan, X., de Pablos, P. O., Sun, Y., & Cao, X. (2015). From e-learning to social-learning: Mapping development of studies on social media-supported knowledge management. Computers in Human Behavior, 51, 803-811.
  • Zimmerman, T. D. (2012). Exploring learner to content interaction as a success factor in online courses. The International Review of Research in Open and Distributed Learning, 13(4), 152. doi:10.19173/irrodl.v13i4.1302

Uzaktan Eğitimde Veri Madenciliği Yöntemi Kullanılarak Yapılmış Araştırmalarda Öğrenme Çıktıları Üzerine Sistematik Bir İnceleme

Year 2022, , 197 - 226, 28.12.2022
https://doi.org/10.53694/bited.1131475

Abstract

Bulunduğumuz çağın ve sürekli değişen teknolojinin sonucunda elde edilen veriler her geçen gün artmaktadır. Bu veriler ile en hızlı, anlamlı ve ileriye yönelik doğru tespitler elde etmek, veri madenciliği ile mümkün olmaktadır. Kısaca ifade etmek gerekirse elde edilen ham bilgiyi veriye, verileri bir sanatkâr gibi işleyip bir esere dönüştürülmesine veri madenciliği olarak tanımlanmaktadır. Veri madenciliği birçok alan için büyük öneme sahiptir. Veri madenciliği sağlık, teknoloji, eğitim gibi geniş kullanım alanları bulunmaktadır. Bu alanların eğitim başlığının kapsamında bulunan alt başlığı ise eğitsel veri madenciliğidir. Eğitsel veri madenciliğinin konusu geleneksel ve uzaktan eğitim çalışmalardır. Bu araştırmada da uzaktan eğitimde veri madenciliği kullanılarak ulaşılan sonuçlardan öğrencilerin öğrenme çıktısına etkisinin ilgili araştırmalardaki eğilimler sonucunda belirlenmesi amaçlanmıştır. Araştırmada uzaktan eğitim ve veri madenciliği kavramları kapsamında Web of Science veri tabanından ulaşılan çalışmaların sistematik incelemesi yer almaktadır. Bu kapsamda bu çalışma literatürdeki çalışmaların analizini sunmayı ve bu yönüyle araştırma uzaktan eğitimin öğrenme çıktılarına etkisinin veri madenciliği ile sonucunu görebilmelerini sağlayacaktır. Ayrıca sistematik inceleme sonucunda araştırma alanında ihtiyaç duyulan çalışmaların belirlenmesi, araştırmacılar için yol gösterici olacağı düşünülmektedir.

References

  • Akcapinar, G., & Bayazit, A. (2018). Investigating video viewing behaviors of students with different learning approaches using video analytics. Turkish Online Journal of Distance Education, 19(4), 116-125.
  • Akçapınar, G. (2014). Çevrimiçi öğrenme ortamındaki etkileşim verilerine göre öğrencilerin akademik performanslarının veri madenciliği yaklaşımı ile modellenmesi. (Yayımlanmamış Doktora Tezi). Hacettepe Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara.
  • Akmeşe, Ö. F., Kör, H., & Erbay, H. (2021). Use of machine learning techniques the forecast of student achievement in higher aducation. Information Technologies and Learning Tools, 82(2), 297-311.
  • Akyürek, M. İ. (2020). Uzaktan Eğitim: Bir Alanyazın taraması. Medeniyet Eğitim Araştırmaları Dergisi, 4(1), 1-9. Retrieved from https://dergipark.org.tr/tr/pub/mead/issue/56310/711904
  • Al-Musharraf, A., & Alkhattabi, M. (2016). An educational data mining approach to explore the effect of using interactive supporting features in an LMS for overall performance within an online learning environment. International Journal of Computer Science and Network Security (IJCSNS), 16(3), 1.
  • Anaya, A. R., Luque, M., & Peinado, M. (2016). A visual recommender tool in a collaborative learning experience. Expert Systems with Applications, 45, 248-259.
  • Andrade, T. L. D., Rigo, S. J., & Barbosa, J. L. V. (2021). Active Methodology, Educational Data Mining and Learning Analytics: A Systematic Mapping Study. Informatics in Education, 20(2).
  • Aydın, S., (2007). Veri madenciliği ve Anadolu Üniversitesi uzaktan eğitim sisteminde bir uygulama. (Yayımlanmamış Doktora Tezi). Anadolu Üniversitesi, Sosyal Bilimler Enstitüsü, Eskişehir.
  • Aydoğdu, Ş. (2020). Educational data mining studies in Turkey: A systematic review. Turkish Online Journal of Distance Education, 21(3), 170-185.
  • Bezerra, L. N. M., & Silva, M. T. (2020). Educational Data Mining Applied to a Massive Course. International Journal of Distance Education Technologies, 18(4), 17–30. doi:10.4018/ijdet.2020100102
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing Teaching and Learning through Educational Data Mining and Learning Analytics: An Issue Brief. Office of Educational Technology, US Department of Education.
  • Bouchet, F., Harley, J. M., Trevors, G. J., & Azevedo, R. (2013). Clustering and profiling students according to their interactions with an intelligent tutoring system fostering self-regulated learning. Journal of Educational Data Mining, 5(1), 104-146. https://doi.org/10.5281/zenodo.3554613
  • Bozkurt, A. (2017). Türkiye’de uzaktan eğitimin dünü, bugünü ve yarını. Açıköğretim Uygulamaları ve Araştırmaları Dergisi, 3(2), 85-124.
  • Chatti, M. A., Dyckhoff, A. L., Schroeder, U., & Thüs, H. (2012). A reference model for learning analytics. International Journal of Technology Enhanced Learning, 4(5), 318-331. doi: 10.1504/IJTEL.2012.051815
  • Chen, H., Dai, Y., Gao, H., Han, D., & Li, S. (2019). Classification and analysis of moocs learner’s state: The study of hidden markov model. Computer Science and Information Systems, 16(3), 849-865.
  • Cheng, L. C., Chu, H. C., & Shiue, B. M. (2015). An innovative approach for assisting teachers in improving instructional strategies via analyzing historical assessment data of students. International Journal of Distance Education Technologies (IJDET), 13(4), 40-61.
  • Cihan, P. (2018). Veri madenciliği yöntemleriyle hayvan hastalıklarında teşhis, prognoz ve risk faktörlerinin belirlenmesi. (Yayımlanmamış Doktora Tezi). Yıldız Teknik Üniversitesi, Fen Bilimleri Enstitüsü, İstanbul.
  • Coşlu, E. (2013). Veri madenciliği. Akademik bilişim, 23-25.
  • Çelebi, V. (2019). Bayes teoremi̇ bağlamında olasılıkçı bayes epi̇stemoloji̇si̇ni̇n kapsamı üzeri̇ne bi̇r ı̇nceleme. FLSF Felsefe ve Sosyal Bilimler Dergisi (28):319–43.
  • Çiltaş, A. (2011). Eğitimde öz-düzenleme öğretiminin önemi üzerine bir çalışma. Mehmet Akif Ersoy Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, 3(5), 1-11.
  • Demirel, M. (1993). Öğrenme stratejilerinin öğretimi. Eğitim ve Bilim, 17(88).
  • Dinçer, S. (2016). Bilgisayar Destekli Eğitim ve Uzaktan Eğitime Genel Bir Bakış. Adana, Seyhan, Türkiye.
  • Erfidan, Ali. (2019). Derslerin uzaktan eğitim yoluyla verilmesiyle ilgili öğretim elemanı ve öğrenci görüşleri Balıkesir Üniversitesi örneği. (Yayınlanmamış Yüksek Lisans Tezi). Balıkesir Üniversitesi Fen Bilimleri Enstitüsü, Balıkesir.
  • Erten, H. (2015). Veri Madenciliği Teknikleri ile Organ Nakli İçin Uygun Donör Oranının Hesaplanması. (Yayınlanmamış Yüksek lisans tezi). Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Fok, W. W., Chen, H., Yi, J., Li, S., Yeung, H. A., Ying, W., & Fang, L. (2014). Data mining application of decision trees for student profiling at the Open University of China. In 2014 IEEE 13th International Conference on Trust, Security and Privacy in Computing and Communications (pp. 732-738). IEEE.
  • Gao, Y., & Zhang, S. (2018). Design of and research on autonomous learning system for distance education based on data mining technology. Educational Sciences: Theory & Practice, 18(6).
  • García, E., Romero, C., Ventura, S., & De Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88.
  • Hämäläinen, W., & Vinni, M. (2010). Classifiers for educational technology. Handbook on educational data mining.
  • Hämäläinen, W., & Vinni, M. (2011). Classifiers for educational data mining. Handbook of Educational Data Mining, Chapman & Hall/CRC Data Mining and Knowledge Discovery Series, 57-71.
  • Hampel, R., & Pleines, C. (2013). Fostering student interaction and engagement in a virtual learning environment: An investigation into activity design and implementation. Calico Journal, 30(3), 342-370.
  • Johnson, L., Smith, R., Willis, H., Levine, A. & Haywood, K. (2011). The 2011 horizon report, Austin, TX: The New Media Consortium.
  • Karaçam, Z. (2013). Sistematik derleme metodolojisi: Sistematik derleme hazırlamak için bir rehber. Dokuz Eylül Üniversitesi Hemşirelik Yüksekokulu Elektronik Dergisi, 6(1), 26-33.
  • Kitchenham, B. (2004). Procedures for performing systematic reviews. Joint technical report Software Engineering Group, Keele University, United Kingdom and Empirical Software Engineering, National ICT Australia Ltd, Australia.
  • Koldere Akın, Y. (2008). Veri madenciliğinde kümeleme algoritmaları ve kümeleme analizi. (Yayınlanmamış Doktora Tezi). Marmara Üniversitesi, Sosyal Bilimler Üniversitesi, İstanbul.
  • Kumtepe, A. T., Atasoy, E., Kaya, Ö., Uğur, S., Dinçer, G. D., Erdoğdu, E., & Aydın, C. H. (2019). An Interaction Framework for Open and Distance Learning: Learning Outcomes, Motivation, Satisfaction, Perception. AJIT-e: Bilişim Teknolojileri Online Dergisi, 10(36), 7-26.
  • 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.
  • Maher, A. (2004). Learning outcomes in higher education: Implications for curriculum design and student learning. Journal of Hospitality, Leisure, Sport and Tourism Education, 3(2), 46-54.
  • Moore, M. G., & Kearsley, G. (2011). Distance education: A systems view of online learning. Cengage Learning.
  • Mullen, G. E., & Tallent-Runnels, M. K. (2006). Student outcomes and perceptions of instructors’ demands and support in online and traditional classrooms. The Internet and Higher Education, 9, 257-266.
  • Newman, M. & Gough, D. (2020). Systematic reviews in educational research: methodology, perspectives and application. In O. Zawacki-Richter, M. Kerres, S. Bedenlier, M. Bond, K. & Buntins (Eds.), Systematic reviews in educational research: Methodology, perspectives and application (pp. 3-22). Wiesbaden: Springer VS.
  • Özbay, Ö. (2015). Veri madenciliği kavramı ve eğitimde veri madenciliği uygulamaları. Uluslararası Eğitim Bilimleri Dergisi, (5), 262-272.
  • Özkan, Y. (2016). Veri Madenciliği Yöntemleri (4.Baskı), Ankara: Papatya Yayınları.
  • Preidys, S., & Sakalauskas, L. (2010). Analysis of students’ study activities in virtual learning environments using data mining methods. Technological and economic development of economy, 16(1), 94-108.
  • Romero, C., & Ventura, S. (2010). Educational data mining: a review of the state of the art. IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), 40(6), 601-618.
  • Romero, C., & Ventura, S. (2012). Data mining in education. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 3(1), 12–27. doi:10.1002/widm.1075
  • Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2010). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135–146. doi:10.1002/cae.20456
  • Romero, C., Espejo, P. G., Zafra, A., Romero, J. R., & Ventura, S. (2013). Web usage mining for predicting final marks of students that use Moodle courses. Computer Applications in Engineering Education, 21(1), 135-146. doi: 10.1002/cae.20456
  • Romero, C., López, M. I., Luna, J. M., & Ventura, S. (2013). Predicting students' final performance from participation in on-line discussion forums. Computers & Education, 68, 458-472.
  • Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. doi: http://dx.doi.org/10.1016/j.compedu.2007.05.016
  • Romero, C., Ventura, S., Espejo, P. G., & Hervás, C. (2008). Data mining algorithms to classify students. In Educational data mining 2008.
  • Sen, B., & Ucar, E. (2012). Evaluating the achievements of computer engineering department of distance education students with data mining methods. Procedia Technology, 1, 262-267.
  • Siemens, G., & Baker, R. S. D. (2012). Learning analytics and educational data mining: towards communication and collaboration. In Proceedings of the 2nd international conference on learning analytics and knowledge (pp. 252-254).
  • Tekin, A. (2018). Tıp'ta veri madenciliği uygulamaları: Yenidoğan sepsisi veri seti analizi/Data mining applications in medicine: Newborn sepsis data set analysis.
  • Trigwell, K., & Prosser, M. (1991). Improving the quality of student learning: the influence of learning context and student approaches to learning on learning outcomes. Higher Education, 22(3), 251–266. doi:10.1007/bf00132290
  • Uzun, Y., Uzun, F. N., & Çakar, E. (2021). Veri madenciliği ve kullanım alanları. Uluslararası Mühendislik, Doğa ve Sosyal Bilimler Sempozyumu, Batman.
  • Viloria, A., López, J. R., Payares, K., Vargas-Mercado, C., Duran, S. E., Hernández-Palma, H., & David, M. A. (2019). Determinating Student Interactions in a Virtual Learning Environment Using Data Mining. Procedia Computer Science, 155, 587–592. doi:10.1016/j.procs.2019.08.082
  • Yasmin, D. (2013). Application of the classification tree model in predicting learner dropout behaviour in open and distance learning. Distance Education, 34(2), 218-231.
  • Yılmaz, K. (2021). Sosyal bilimlerde ve eğitim bilimlerinde sistematik derleme, meta değerlendirme ve bibliyometrik analizler. Manas Sosyal Araştırmalar Dergisi, 10(2), 1457-1490.
  • Yilmaz, R. (2017). Problems experienced in evaluating success and performance in distance education: A case study. Turkish Online Journal of Distance Education, 18(1), 39-51.
  • Yurdugül, H.& Menzi Çetin, N. (2015). Investigation of the relationship between learning process and learning outcomes in e-learning environments. Eurasian Journal of Educational Research, 59, 57-74. http://dx.doi.org/10.14689/ejer.2015.59.4
  • Yurtoğlu, H. (2005). Yapay Sinir Ağları Modellemesi ile Öngörü Modellemesi: Bazı Makroekonomik Değişkenler için Türkiye Örneği. (Uzmanlık Tezi). DPT, Ankara.
  • Zang, W., & Lin, F. (2003, August). Investigation of web-based teaching and learning by boosting algorithms. In International Conference on Information Technology: Research and Education, 2003. Proceedings. ITRE2003. (pp. 445-449). IEEE.
  • Zhang, X., Gao, Y., Yan, X., de Pablos, P. O., Sun, Y., & Cao, X. (2015). From e-learning to social-learning: Mapping development of studies on social media-supported knowledge management. Computers in Human Behavior, 51, 803-811.
  • Zimmerman, T. D. (2012). Exploring learner to content interaction as a success factor in online courses. The International Review of Research in Open and Distributed Learning, 13(4), 152. doi:10.19173/irrodl.v13i4.1302
There are 64 citations in total.

Details

Primary Language Turkish
Subjects Other Fields of Education
Journal Section Research Articles
Authors

Elif Akgün 0000-0003-2580-9896

Özlem Maral 0000-0002-2378-6945

Publication Date December 28, 2022
Submission Date June 15, 2022
Acceptance Date December 18, 2022
Published in Issue Year 2022

Cite

APA Akgün, E., & Maral, Ö. (2022). Uzaktan Eğitimde Veri Madenciliği Yöntemi Kullanılarak Yapılmış Araştırmalarda Öğrenme Çıktıları Üzerine Sistematik Bir İnceleme. Bilgi Ve İletişim Teknolojileri Dergisi, 4(2), 197-226. https://doi.org/10.53694/bited.1131475


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Bilgi ve İletişim Teknolojileri Dergisi (BİTED)

Journal of Information and Communication Technologies