Çevrimiçi Öğrenenlerin E-öğrenme Ortamı Etkileşimlerinin Öğrenen Kontrolüne Dayalı Olarak İncelenmesi

Yıl 2022, Sayı: 54, 248 - 271, 02.01.2022

Muhittin Şahin , Halil Yurdugül

https://doi.org/10.9779/pauefd.792252

Cited By: 1

Öz

Öğrenenlere öğrenme süreçlerini etkili ve verimli hale getirebilmek amacıyla geçmişten günümüze birçok teknoloji destekli öğrenme ortamı sunulmaktadır. Bu öğrenme ortamlarından son zamanlarda yükseköğretimde sıklıkla kullanılanlardan bir tanesi de Öğrenme Yönetim Sistemleridir (ÖYS). Ancak bu öğrenme ortamlarından öğrenenlerin etkili bir şekilde yararlanabilmesi için bazı özelliklere sahip olması gerekmektedir. Bu özelliklerden bir tanesi de öğrenen kontrolüdür. Araştırma kapsamında öğrenenlerin ÖYS etkileşimlerinin öğrenen kontrolünün yüksek ya da düşük olmasına göre farklılık gösterip göstermediği incelenmiştir. Bunu inceleyebilmek amacıyla 14 haftalık bir öğrenme yaşantısı geçiren öğrenenlerin etkileşim verileri irdelenmiştir. Etkileşim verileri hem genel etkileşimleri hem de temalar bağlamında incelenmiştir. Öğrenenlerin çevrimiçi öğrenme ortamlarındaki etkileşimleri; a) öğrenen-içerik, b) öğrenen-değerlendirme ve c) öğrenen-öğrenen (öğrenen-tartışma) alt temaları bağlamında irdelenmiştir. Bu amaçla standartlaştırılmış artıklar analizi işe koşulmuştur. Bu analiz ile 2x2’den daha geniş olumsallık tabloları incelenebilmektedir. Bir diğer deyiş ile ki-kare analizinin post-hoc analizleri olduğu ifade edilebilir. Elde edilen bulgulara göre öğrenenlerin sistem ile genel etkileşimlerinin öğrenen kontrolüne göre birçok haftada farklılık gösterdiği bulgusuna ulaşılmıştır. İçerik, öğrenen ve tartışma temalarına bakıldığında ise öğrenen kontrolü yüksek olan öğrenenlerin haftalık bazda daha fazla etkileşime girme eğiliminde oldukları ancak bunun genel olarak istatisiksel anlamda farklı olmadığı da bir diğer bulgudur. Araştırmada elde edilen bulgular çevrimiçi öğrenme ortamlarının nasıl daha etkili ve verimli bir hale getirilebileceği bağlamında tartışılmıştır.

Anahtar Kelimeler

Çevrimiçi öğrenme ortamları, öğrenen kontrolü, standartlaştırılmış artıklar, etkileşim verileri

Examination of Online Learners' Interactions in the E-learning Environment Based on Level of Learner Control

Öz

Instructional technologies provide significant opportunities to researchers to facilitate learners' learning and to design more effective learning environments and experiences. In this context, nowadays it is seen that LMS is being used especially in higher education institutions intensively. However, learners must have some individual characteristics in order to benefit from these learning environments effectively. One of these learner’s characteristics is learner control. Within the scope of this research, it was examined whether the LMS interactions of the learners differ according to the high or low learner control level. In order to investigate this, the interaction data of the learners who had a 14-week learning experience were examined. Interaction data were analyzed based on both general interactions and interaction themes. Interactions of learners in online learning environments were investigated such as a) learner-content, b) learner-assessment and c) learner-learner (learner-discussion) sub-themes. For this purpose, standardized residual analysis was conducted. Contingency tables larger than 2x2 can be examined via standardized residual analysis. In other words, it can be stated that this analysis is a post-hoc analysis of chi-square analysis. According to the findings, it was found that the overall interactions of the learners with the system differ in many weeks based on learner control level. Content, learner, and discussion themes are examined, it is another finding that learners who have high-level learner control tend to interact more weekly, but this is not statistically different in general. The findings were discussed how online learning environments can be made more effective and efficient.

Anahtar Kelimeler

online learning environments, learner control, standardized residuals, log data

Kaynakça

• Alsancak Sırakaya, D., & Yurdugül, H. (2016). Öğretmen Adaylarının Çevrimiçi Öğrenme Hazır Bulunuşluluk Düzeylerinin İncelenmesi: Ahi Evran Üniversitesi Örneği. Journal of Kirsehir Education Faculty, 17(1).
• Arkün, S., & Aşkar, P. (2010). Yapılandırmacı öğrenme ortamlarını değerlendirme ölçeğinin geliştirilmesi. Hacettepe Üniversitesi Eğitim Fakültesi Dergisi, 39(39), 32-43.
• Bewick, V., Cheek, L., & Ball, J. (2003). Statistics review 8: Qualitative data–tests of association. Critical Care, 8(1), 46.
• Bloom, B. S. (1976). Human characteristics and school learning. McGraw-Hill.
• Brown, M., Dehoney, J., & Millichap, N. (2015). The next generation digital learning environment. A Report on Research. ELI Paper. Louisville, CO: Educause April.
• Campanizzi, J. A. (1978). Effects of locus of control and provision of overviews in a computer-assisted instruction sequence. AEDS Journal, 12(1), 21-30.
• Chang, M. M., & Ho, C. M. (2009). Effects of locus of control and learner-control on web-based language learning. Computer Assisted Language Learning, 22(3), 189-206.
• Chang, M., & Ho, C. (2009). Effects of locus of control and learner-control on web-based language learning. Computer Assisted Language Learning, 22(3), 189–206.
• Cornell Statistical Consulting Unit. (2018). Adjusted standardized residuals for interpreting contingency tables (Report No. 95). Retrieved from https://www.cscu.cornell.edu/news/statnews/stnews95.pdf
• Çakır, Ö., & Horzum, M. B. (2015). The examination of the readiness levels of teacher candidates for online learning in terms of various variables/Öğretmen adaylarının çevrimiçi öğrenmeye hazır bulunuşluk düzeylerinin çeşitli değişkenler açısından incelenmesi. Eğitimde Kuram ve Uygulama, 11(1), 1-15.
• El-Tigi, M., & Branch, R. M. (1997). Designing for interaction, learner control, and feedback during web-based learning. Educational Technology, 37(3), 23-29.
• Field, A. (2018). Discovering Statistics Using IBM SPSS Statistics.
• Garcia-Perez, M. A., & Nunez-Anton, V. (2003). Cellwise residual analysis in two-way contingency tables. Educational and Psychological Measurement, 63(5), 825-839.
• Gray, S. H. (1987). The effect of sequence control on computer assisted learning. Journal of Computer-Based Instruction, 14(2), 54–56.
• Holmes, B., & Gardner, J. (2006). E-learning: Concepts and practice. Sage.
• Horzum, M. B., Demir Kaymak, Z., & Güngören, Ö. C. (2015). Structural equation modeling towards online learning readiness. Academic motivations, and perceived learning. Educational Sciences: Theory & Practice, 15(3), 759-770
• Howell, D. C. (2012). Statistical methods for psychology. Cengage Learning.
• Huang, H. M. (2002). Toward constructivism for adult learners in online learning environments. British Journal of Educational Technology, 33(1), 27-37.
• Hung, M. L., Chou, C., Chen, C. H., & Own, Z. Y. (2010). Learner readiness for online learning: Scale development and student perceptions. Computers & Education, 55(3), 1080-1090.
• Keskin, S., Şahin, M., & Yurdugül, H. (2019). Online Learners’ Navigational Patterns Based on Data Mining in Terms of Learning Achievement. In Learning Technologies for Transforming Large-Scale Teaching, Learning, and Assessment (pp. 105-121). Springer, Cham.
• Kiu, C. C. (2018). Supervised educational data mining to discover students’ learning process to improve students’ performance. In Redesigning Learning for Greater Social Impact (pp. 249-258). Springer, Singapore.
• Knowles, M. S. (1975). Self-directed learning: A guide for learners and teachers. New York: Association Press.
• MacDonald, P. L., & Gardner, R. C. (2000). Type I error rate comparisons of post hoc procedures for I j Chi-Square tables. Educational and Psychological Measurement, 60(5), 735-754.
• Martindale, T., & Dowdy, M. (2010). Personal learning environments. Emerging technologies in distance education, 177-193.
• Means, B., Toyama, Y., Murphy, R., Bakia, M., & Jones, K. (2009). Evaluation of evidence-based
• Meiselwitz, G., & Sadera, W. (2008). Investigating the connection between usability and learning outcomes in online learning environments. Journal of Online Learning and Teaching, 4(2), 9.
• Merrill, M. D. (1975). Learner control: Beyond aptitude-treatment interactions. AV Communication Review, 23(2), 217-226.
• Merrill, M. D., & Twitchell, D. (1994). Instructional design theory. Educational Technology. Practices in online learning: A meta-analysis and review of online learning studies.
• Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1-7.
• Ratnapala, I. P., Ragel, R. G., & Deegalla, S. (2014, December). Students behavioural analysis in an online learning environment using data mining. In 7th International Conference on Information and Automation for Sustainability (pp. 1-7). IEEE.
• Scheiter, K., & Gerjets, P. (2007). Learner control in hypermedia environments. Educational Psychology Review, 19(3), 285-307.
• Shyu, H. (1993). Effects of learner control and learner characteristics on learning a procedural task (Yayımlanmamış yüksek lisans tezi). University of Connecticut, ABD.
• Shyu, H. Y., & Brown, S. W. (1992). Learner control versus program control in interactive videodisc instruction: What are the effects in procedural learning. International Journal of Instructional Media, 19(2), 85-95.
• Smith, P. J. (2005). Learning preferences and readiness for online learning. Educational psychology, 25(1), 3-12.
• Şahin, M., Keskin, S., & Yurdugül, H. (2018). Online learners’ readiness and learning interactions: A sequential analysis. Cognition and Exploratory Learning in Learning the Digital Age (CELDA 2018), 38.
• Şahin, M., Keskin, S., Özgür, A., & Yurdugül, H. (2017). E-öğrenme ortamlarında öğrenen özelliklerine dayalı etkileşim profillerinin belirlenmesi. Eğitim Teknolojisi Kuram ve Uygulama, 7(2), 172-192.
• Taipjutorus, W., Hansen, S., & Brown, M. (2012). Investigating a relationship between learner control and self-efficacy in an online learning environment. Journal of Open, Flexible, and Distance Learning, 16(1), 56-69.
• Tennyson, R. D., & Buttrey, T. (1980). Advisement and management strategies as design variables in computer-assisted instruction. Educational Communication and Technology Journal-ECTJ, 28(3), 169.
• Tian, F., Wang, S., Zheng, C., & Zheng, Q. (2008, April). Research on e-learner personality grouping based on fuzzy clustering analysis. In 2008 12th International Conference on Computer Supported Cooperative Work in Design (pp. 1035-1040). IEEE.
• Waltz CF, Strickland OL, Lenz ER. Operationalizing nursing concepts. In: Waltz CF, Strickland OL, Lenz ER (eds). Measurement in Nursing and Health Research,3rd Ed. New York: Springer Publishing Company, 2005; 22–41.
• Waltz, C. F., Strickland, O. L., & Lenz, E. R. (Eds.). (2010). Measurement in nursing and health research. Springer publishing company.
• Wang, L. C. C., & Beasley, W. (2002). Effects of learner control and hypermedia preference on cyber-students performance in a Web-based learning environment. Journal of Educational Multimedia and Hypermedia, 11(1), 71-91.
• Warner, D., Christie, G., & Choy, S. (1998). Readiness of VET clients for flexible delivery including on-line learning. Brisbane: Australian National Training Authority. Washington, DC, U.S. Department of Education.
• Williams, M. D. (1996). Learner-control and instructional technologies. Handbook of research for educational communications and technology, 2, 957-983.
• Zhou, M. (2010, May). Data Mining and Student e-Learning Profiles. In 2010 International Conference on E-Business and E-Government (pp. 5405-5408). IEEE.