Araştırma Makalesi
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The Analysis of Student Behaviors in Virtual Learning Environments by Clustering Method

Yıl 2019, Cilt: 16 Sayı: 1, 725 - 743, 25.12.2019

Öz

Virtual learning environments offer a wide range of opportunities for educators and students, providing the appropriate tools for managing the learning process and ensuring that students are responsible for their own learning. Despite all these opportunities, even within the same educational institution, it is seen that virtual learning environments are not used effectively. In this experimental research, in Atılım University, Ankara where Moodle is used as a virtual learning environment, the use of the environment by the users is analyzed by data mining techniques using clustering algorithms, and it is determined which are preferred or less preferred components in the environment. The research was conducted on 131 students who attended the Introduction to Computers and Information Systems course at a university in Ankara. During the practicing process, the students took a face to face course per week and the rest of the course in the Moodle virtual learning environment at the same time by the blended learning approach. In the analysis of the data, it was determined how the students were distributed according to their satisfaction with the virtual learning environment, computer anxiety and academic achievement by using k-means analysis from clustering algorithms. In addition, according to this distribution, the use of activity in the virtual learning environment is evaluated whether there is a difference between them. 

Kaynakça

  • Agnihotri, L. (2014). Building a student at-risk model: An end-to-end perspective. In Proc. 7th International Conference on Educational Data Mining.
  • Australian Flexible Learning Framework. (2007). Supporting e-learning Opportunities. Web sitesi: http://ldt.eworks.edu.au/
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, D.C. Web sitesi: https://www.researchgate.net/publication/320614434_
  • Casey K. & Gibson P.. (2010) (m)oodles of data: Mining moodle to understand student behaviour. In Proc. 3rd Irish Conference on Engaging Pedagogy.
  • Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270-295.
  • Cerezo R., Sanchez-Santillan M., Nunez J.C., & Paule M.P.(2015). Different patterns of students’ interaction with moodle and their relationship with achievement. In Proc. 8th International Conference on Educational Data Mining.
  • Cristóbal, R., Sebastián, V., Mykola, P. & Ryan, S. J. D. B. (2010). Introduction Handbook of Educational Data Mining (pp. 1-6): CRC Press.
  • Dilekman, M. ve Ada, Ş. (2005). Öğrenmede Güdülenme. Kazım Karabekir Eğitim Fakültesi Dergisi, 11, 113-123.
  • El-Deghaidy, H., & Nouby, A. (2008). Effectiveness of a blended e-learning cooperative approach in an Egyptian teacher education programme. Computers & Education, 51, 988-1006.
  • Ergül, E. (2013). Bilişim Teknolojileri Öğretmen Adaylarının Moodle İle Ders İşlenmesi Hakkındaki Görüşleri, Yüksek Lisans Tezi, Eğitim Teknolojileri Anabilim Dalı, Isparta.
  • Erten, H. (2015). Veri Madenciliği Teknikleri İle Organ Nakli İçin Uygun Donör Oranının Hesaplanması, Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Eryılmaz, M. (2011). Uyarlanabilir İçerik ve Uyarlanabilir Gezinmenin Öğrenci Doyumu ve Bilişsel Yüke Etkileri, Eğitim Bilimleri ve Uygulama Dergisi, 20.
  • Floyd, C., Schultz, T. & Fulton, S. (2012). “Security Vulnerabilities in The Open Source Moodle E-learning System”, Proceedings of the 16th Colloquium for Information Systems Security Education, Lake Buena Vista, Florida, s. 42-47.
  • Garnham, C. & Kaleta, R. (2002). Introduction to Hybrid Courses. Teaching With Technology Today, 8 (6).
  • Gašević,D., Dawson, S.,Rogers,T.(2016). Learning analytics should not promote one sizefits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. Web sitesi : http://dx.doi.org/10.1016/j.iheduc.2015.10.002
  • García, E., Romero, C., Ventura, S. ve de Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88. doi: 10.1016/j.iheduc.2010.07.006
  • Greller,W. & Drachsler,H.(2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 6(3),42-57
  • Karasar, N. (2012). “Bilimsel Araştırma Yöntemi”, Nobel Yayın Dağıtım, 24. Baskı, s.76-81, Ankara.
  • Kışla T., Karaoğlan B., Bozok Algin G., Candemir C. (2014). Harmanlanmış Öğrenme Ortamında Moodle Platformunun Kullanılması Ile Ilgılı Paydaş Görüşlerının Incelenmesı, Eğitim ve Öğretim Araştırmaları Dergisi (Journal of Research in Education and Teaching), Cilt: 3 Sayı: 4 Makale No:15 sayfa: 154-167.
  • Lonn, S., Teasley, S.D. & Krumm, A. E. (2011). Who Needs To Do What Where?: Using Learning Management Systems On Residential vs. Commuter Campuses, Computers & Education, 56, 642–649.
  • Leony, D., Pardo, A., Valentin, L. F., Quinones, I. & Kloos, C.D. (2012). Learning Analytics In The LMS: Using Browser Extensions To Embed Visualizations Into A Learning Management System, CEUR Workshop Proceedings, Web sitesi: http://ceurws.org/Vol894/paper6.pdf.
  • Mansouri, M. (2003). Perceptions of First –Time Participants in a StateAgency-Sponsored Online Graduate Program and Their Implications for Online Education Planning, Development and Support. VirginiaCommonwealth University School of Education. Unpublished Doctoral Dissertaiton.
  • Mcdonald, A. S. (2001) The Prevalence And Effects Of Test Anxiety İn School Children. Educational Psychology, An International Journal of Experimental Educational Psychology, 21 (1), 89-101. Murray, D. (2001). E-Learning for the Workplace. Creating Canada’s LifelongLearners. Web sitesi: http://en.copian.ca/library/research/cboc/aliant/aliant.pdf
  • Osguthorpe, R. T. & Graham, C. R., (2003). Blended Learning Environments Definitions and Directions. The Quarterly Review of Distance Education, 4(3), 227-233.
  • Reis, A.Z., Baktır, H.Ö., Çelik, B., Erkoç, M.F., Özçakır, F.C., Özdemir, Ş. ve Şahin, K. (2012). Açık Kaynak Kodlu Öğrenme Yönetim Sistemleri Üzerine Bir Karşılaştırma Çalışması, Eğitim ve Öğretim Araştırmaları Dergisi, Cilt: 1, Sayı: 2, s. 42-58.
  • 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.
  • Romero,C.& Ventura, S.,(2013).Data mining in education. Wiley InterdisciplinaryReviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
  • Saadé RG., & Kira D.(2009). Computer anxiety in e-learning: The effect of computer selfefficacy. Journal of Information Technology Education 8.177-191.
  • Sael N., Marzak A. & Behja H. (2013). Multilevel clustering and association rule mining for learners’ profiles analysis. International Journal of Computer Science Issues (IJCSI), 10(3).
  • Saenz, V. B.; Kim, S.; Valdez, P.; Hatch, D.; Lee, K. ve Bukoski, B. E. (2011). Community college student engagement patterns: a typology revealed through exploratory cluster analysis. Community College Review, 39(3), 235-267.
  • San Diego, J.P., Ballard, J., Hatzipanagos, S., Webb, M., Khan, E., Blake, P., Dore, T., Konstantinidis, A., & Barrett , I. (2012). Do Moodle analytics have a role to play in learning design, assessment and feedback?, 1 st Moodle ResearchConference, September, 14 – 15,
  • Heraklion, Greece. Semerci, A., ve Keser, H. (2013). E-öğrenme bariyerleri. Türkiye’de E-Öğrenme: Gelişmeler ve Uygulamalar, IV, Ocak 2013, Eskişehir, 105-123.
  • Siemens G. & Long P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review 46 (5).
  • Tanaka, A., Takehara, T. & Yamauchi, H. (2006). Achievement Goals İn A Presentation Task: Performance Expectancy, Achievement Goals, State Anxiety, And Task Performance, Learning and Individual Differences, 16, 93–99.
  • Talavera, L.& Gaudioso, E. (2004). Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces, 16th European Conference on Artificial Intelligence (ECAI 2004) - Workshop on Artificial Intelligence, 17–23, Valencia, Spain.
  • Ünal, T.A. (2014). Büyük Veri Ve Eğitimsel Veri Madenciliğinin Eğitim Alanına Katkılarının İncelenmesi, 8th International Computer & Instructional Technologies Symposium, Trakya University Edirne.
  • Whitmer, J., Fernandes, K. & Allen, W.R. (2012). Analytics in Progress: Technology Use, Student Characteristics, and Student Achievement, Educase, Web sitesi: http://www.educause.edu/ero/article/analytics-progress-technology-usestudentcharacteristics-and-student-achievement.

Sanal Öğrenme Ortamlarındaki Öğrenci Davranışlarının Kümeleme Yöntemi İle Analiz Edilmesi

Yıl 2019, Cilt: 16 Sayı: 1, 725 - 743, 25.12.2019

Öz

Öğrenme sürecini yönetmek ve öğrencilerin kendi öğrenmelerinden sorumlu olmalarını sağlamak amacına yönelik uygun araçlar sunan sanal öğrenme ortamları, bu özellikleri ile eğitimci ve öğrencilere geniş imkanlar sağlamaktadır. Tüm bu imkânlara rağmen aynı öğretim kurumu içerisinde sanal öğrenme ortamlarının yeterince etkili kullanılmadığı görülmektedir. Bu deneysel araştırmada sanal öğrenme ortamı olarak Moodle kullanılan bir üniversitede, kullanıcıların ortamı kullanma bilgileri veri madenciliği tekniklerinden kümeleme algoritmaları ile analiz edilerek, ortamda kullanılması tercih edilmeyen ya da daha az tercih edilen bileşenler tespit edilmiştir. Araştırmaya Ankara Atılım Üniversitesi Fen ve Edebiyat Fakültesi’nde Bilgisayara ve Bilgi Sistemlerine Giriş dersini alan 131 öğrenci katılmıştır. Uygulama sürecinde tüm öğrenciler dersi haftada bir kez yüz yüze geri kalanını Moodle sanal öğrenme ortamında olmak üzere harmanlanmış öğrenme yaklaşımı ile almışlardır. Verilerin analizinde kümeleme algoritmalarından k-ortalamalar analizi kullanılmıştır. Kümeleme analizi ile öğrencilerin sanal öğrenme ortamına yönelik memnuniyetleri, bilgisayar kaygıları ve yıl sonu akademik başarılarına göre nasıl bir dağılım gösterdikleri belirlenmiştir. Ayrıca bu dağılıma göre sanal öğrenme ortamındaki aktivite kullanımları arasında farklılık olup olmadığı değerlendirilmiştir.

Kaynakça

  • Agnihotri, L. (2014). Building a student at-risk model: An end-to-end perspective. In Proc. 7th International Conference on Educational Data Mining.
  • Australian Flexible Learning Framework. (2007). Supporting e-learning Opportunities. Web sitesi: http://ldt.eworks.edu.au/
  • Bienkowski, M., Feng, M., & Means, B. (2012). Enhancing teaching and learning through educational data mining and learning analytics: An issue brief. Washington, D.C. Web sitesi: https://www.researchgate.net/publication/320614434_
  • Casey K. & Gibson P.. (2010) (m)oodles of data: Mining moodle to understand student behaviour. In Proc. 3rd Irish Conference on Engaging Pedagogy.
  • Cassady, J. C., & Johnson, R. E. (2002). Cognitive test anxiety and academic performance. Contemporary Educational Psychology, 27(2), 270-295.
  • Cerezo R., Sanchez-Santillan M., Nunez J.C., & Paule M.P.(2015). Different patterns of students’ interaction with moodle and their relationship with achievement. In Proc. 8th International Conference on Educational Data Mining.
  • Cristóbal, R., Sebastián, V., Mykola, P. & Ryan, S. J. D. B. (2010). Introduction Handbook of Educational Data Mining (pp. 1-6): CRC Press.
  • Dilekman, M. ve Ada, Ş. (2005). Öğrenmede Güdülenme. Kazım Karabekir Eğitim Fakültesi Dergisi, 11, 113-123.
  • El-Deghaidy, H., & Nouby, A. (2008). Effectiveness of a blended e-learning cooperative approach in an Egyptian teacher education programme. Computers & Education, 51, 988-1006.
  • Ergül, E. (2013). Bilişim Teknolojileri Öğretmen Adaylarının Moodle İle Ders İşlenmesi Hakkındaki Görüşleri, Yüksek Lisans Tezi, Eğitim Teknolojileri Anabilim Dalı, Isparta.
  • Erten, H. (2015). Veri Madenciliği Teknikleri İle Organ Nakli İçin Uygun Donör Oranının Hesaplanması, Yüksek Lisans Tezi, Gazi Üniversitesi Fen Bilimleri Enstitüsü, Ankara.
  • Eryılmaz, M. (2011). Uyarlanabilir İçerik ve Uyarlanabilir Gezinmenin Öğrenci Doyumu ve Bilişsel Yüke Etkileri, Eğitim Bilimleri ve Uygulama Dergisi, 20.
  • Floyd, C., Schultz, T. & Fulton, S. (2012). “Security Vulnerabilities in The Open Source Moodle E-learning System”, Proceedings of the 16th Colloquium for Information Systems Security Education, Lake Buena Vista, Florida, s. 42-47.
  • Garnham, C. & Kaleta, R. (2002). Introduction to Hybrid Courses. Teaching With Technology Today, 8 (6).
  • Gašević,D., Dawson, S.,Rogers,T.(2016). Learning analytics should not promote one sizefits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84. Web sitesi : http://dx.doi.org/10.1016/j.iheduc.2015.10.002
  • García, E., Romero, C., Ventura, S. ve de Castro, C. (2011). A collaborative educational association rule mining tool. The Internet and Higher Education, 14(2), 77-88. doi: 10.1016/j.iheduc.2010.07.006
  • Greller,W. & Drachsler,H.(2012). Translating Learning into Numbers: A Generic Framework for Learning Analytics. Educational Technology & Society, 6(3),42-57
  • Karasar, N. (2012). “Bilimsel Araştırma Yöntemi”, Nobel Yayın Dağıtım, 24. Baskı, s.76-81, Ankara.
  • Kışla T., Karaoğlan B., Bozok Algin G., Candemir C. (2014). Harmanlanmış Öğrenme Ortamında Moodle Platformunun Kullanılması Ile Ilgılı Paydaş Görüşlerının Incelenmesı, Eğitim ve Öğretim Araştırmaları Dergisi (Journal of Research in Education and Teaching), Cilt: 3 Sayı: 4 Makale No:15 sayfa: 154-167.
  • Lonn, S., Teasley, S.D. & Krumm, A. E. (2011). Who Needs To Do What Where?: Using Learning Management Systems On Residential vs. Commuter Campuses, Computers & Education, 56, 642–649.
  • Leony, D., Pardo, A., Valentin, L. F., Quinones, I. & Kloos, C.D. (2012). Learning Analytics In The LMS: Using Browser Extensions To Embed Visualizations Into A Learning Management System, CEUR Workshop Proceedings, Web sitesi: http://ceurws.org/Vol894/paper6.pdf.
  • Mansouri, M. (2003). Perceptions of First –Time Participants in a StateAgency-Sponsored Online Graduate Program and Their Implications for Online Education Planning, Development and Support. VirginiaCommonwealth University School of Education. Unpublished Doctoral Dissertaiton.
  • Mcdonald, A. S. (2001) The Prevalence And Effects Of Test Anxiety İn School Children. Educational Psychology, An International Journal of Experimental Educational Psychology, 21 (1), 89-101. Murray, D. (2001). E-Learning for the Workplace. Creating Canada’s LifelongLearners. Web sitesi: http://en.copian.ca/library/research/cboc/aliant/aliant.pdf
  • Osguthorpe, R. T. & Graham, C. R., (2003). Blended Learning Environments Definitions and Directions. The Quarterly Review of Distance Education, 4(3), 227-233.
  • Reis, A.Z., Baktır, H.Ö., Çelik, B., Erkoç, M.F., Özçakır, F.C., Özdemir, Ş. ve Şahin, K. (2012). Açık Kaynak Kodlu Öğrenme Yönetim Sistemleri Üzerine Bir Karşılaştırma Çalışması, Eğitim ve Öğretim Araştırmaları Dergisi, Cilt: 1, Sayı: 2, s. 42-58.
  • 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.
  • Romero,C.& Ventura, S.,(2013).Data mining in education. Wiley InterdisciplinaryReviews: Data Mining and Knowledge Discovery, 3(1), 12-27.
  • Saadé RG., & Kira D.(2009). Computer anxiety in e-learning: The effect of computer selfefficacy. Journal of Information Technology Education 8.177-191.
  • Sael N., Marzak A. & Behja H. (2013). Multilevel clustering and association rule mining for learners’ profiles analysis. International Journal of Computer Science Issues (IJCSI), 10(3).
  • Saenz, V. B.; Kim, S.; Valdez, P.; Hatch, D.; Lee, K. ve Bukoski, B. E. (2011). Community college student engagement patterns: a typology revealed through exploratory cluster analysis. Community College Review, 39(3), 235-267.
  • San Diego, J.P., Ballard, J., Hatzipanagos, S., Webb, M., Khan, E., Blake, P., Dore, T., Konstantinidis, A., & Barrett , I. (2012). Do Moodle analytics have a role to play in learning design, assessment and feedback?, 1 st Moodle ResearchConference, September, 14 – 15,
  • Heraklion, Greece. Semerci, A., ve Keser, H. (2013). E-öğrenme bariyerleri. Türkiye’de E-Öğrenme: Gelişmeler ve Uygulamalar, IV, Ocak 2013, Eskişehir, 105-123.
  • Siemens G. & Long P. (2011). Penetrating the fog: Analytics in learning and education. EDUCAUSE Review 46 (5).
  • Tanaka, A., Takehara, T. & Yamauchi, H. (2006). Achievement Goals İn A Presentation Task: Performance Expectancy, Achievement Goals, State Anxiety, And Task Performance, Learning and Individual Differences, 16, 93–99.
  • Talavera, L.& Gaudioso, E. (2004). Mining Student Data to Characterize Similar Behavior Groups in Unstructured Collaboration Spaces, 16th European Conference on Artificial Intelligence (ECAI 2004) - Workshop on Artificial Intelligence, 17–23, Valencia, Spain.
  • Ünal, T.A. (2014). Büyük Veri Ve Eğitimsel Veri Madenciliğinin Eğitim Alanına Katkılarının İncelenmesi, 8th International Computer & Instructional Technologies Symposium, Trakya University Edirne.
  • Whitmer, J., Fernandes, K. & Allen, W.R. (2012). Analytics in Progress: Technology Use, Student Characteristics, and Student Achievement, Educase, Web sitesi: http://www.educause.edu/ero/article/analytics-progress-technology-usestudentcharacteristics-and-student-achievement.
Toplam 37 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Bölüm Makaleler
Yazarlar

Meltem Eryılmaz

Yayımlanma Tarihi 25 Aralık 2019
Yayımlandığı Sayı Yıl 2019 Cilt: 16 Sayı: 1

Kaynak Göster

APA Eryılmaz, M. (2019). Sanal Öğrenme Ortamlarındaki Öğrenci Davranışlarının Kümeleme Yöntemi İle Analiz Edilmesi. Van Yüzüncü Yıl Üniversitesi Eğitim Fakültesi Dergisi, 16(1), 725-743.