Öğrenme Analitiği Geribildirimleri ile Desteklenmiş Ters-Yüz Öğrenme Ortamının Çeşitli Değişkenler Açısından Modellenmesi

Year 2020, Volume: 2 Issue: 1, 1 - 16, 22.06.2020

Fatma Gizem Karaoğlan Yılmaz

Abstract

Bu araştırmanın amacı öğrenme analitiklerine dayalı kişiselleştirilmiş tavsiye ve yönlendirme mesajları ile desteklenmiş bir ters-yüz öğrenme ortamında öğrencilerin sorgulama topluluğu, akademik öz-yeterlikleri, yansıtıcı düşünme becerileri, problem çözme becerileri ve üstbilişsel farkındalıkları arasındaki ilişkileri incelemektir. Bu amaçla öğrenme yönetim sistemi üzerinden öğrencilerin haftalık öğrenme analitiği sonuçlarına dayalı olarak kişiselleştirilmiş tavsiye ve yönlendirmelerde bulunulmuştur. Araştırmada öğrencilerin sorgulama topluluğu, akademik öz-yeterlikleri, yansıtıcı düşünme becerileri, problem çözme becerileri ve üstbilişsel farkındalık durumları belirlenmiş ve bunlar arasındaki yapısal ilişkiler ortaya konulmaya çalışılmıştır. Araştırma, ters-yüz öğrenme yaklaşımı ile yürütülen Bilgisayar I dersinde gerçekleştirilmiştir. Araştırma 117 üniversite öğrencisi üzerinde yürütülmüştür. Araştırma sonucunda öğrencilerin sorgulama topluluğu düzeylerinin yüksek; akademik öz-yeterlikleri, yansıtıcı düşünme becerileri, problem çözme becerileri ve üstbilişsel farkındalık düzeylerinin ise orta olduğu görülmüştür. Sorgulama topluluğunun akademik öz-yeterliği, akademik öz-yeterliğin yansıtıcı düşünme becerileri ile problem çözme becerilerini anlamlı şekilde etkilediği görülmüştür. Yansıtıcı düşünme becerileri ile problem çözme becerilerinin ise üstbilişsel farkındalığı anlamlı şekilde etkilediği ortaya konulmuştur. Bu araştırma öğrenme analitiklerine dayalı kişiselleştirilmiş tavsiye ve yönlendirme mesajları ile desteklenmiş bir ters-yüz öğrenme ortamında yapısal değişkenler arasındaki ilişkileri ortaya koyması açısından önemlidir.

Keywords

Öğrenme analitiği, Sorgulama topluluğu, Akademik öz-yeterlik, Yansıtıcı düşünme, Problem çözme, Üstbilişsel farkındalık, ters-yüz öğrenme, geribildirim

Modeling Different Variables in Flipped Classrooms Supported with Learning Analytics Feedback

Abstract

The aim of this research is to examine the relationships between students' community of inquiry, academic self-efficacy, reflective thinking skills, problem-solving skills, and metacognitive awareness in a flipped learning environment supported by personalized recommendation and guidance messages based on learning analytics. For this purpose, personalized recommendation and guidance were made based on the weekly learning analytics results of the students through the learning management system. In the research, the status of community of inquiry, academic self-efficacy, reflective thinking skills, problem-solving skills and metacognitive awareness were determined and the structural relationships between them were tried to be revealed. The research was carried out in the Computer I course, carried out with the flipped learning approach. The research was carried out on 117 university students. As a result of the research, students' community of inquiry levels are high; academic self-efficacy, reflective thinking skills, problem-solving skills and metacognitive awareness levels were found to be moderate. It has been observed that community of inquiry has a significant effect on academic self-efficacy. Academic self-efficacy was found to significantly affect reflective thinking skills and problem-solving skills. It was demonstrated that reflective thinking skills and problem-solving skills significantly affect metacognitive awareness. This research is important in terms of revealing the relationships between structural variables in flipped learning supported by personalized recommendation and guidance feedbacks based on learning analytics.

Keywords

Learning analytics, Community of inquiry, Academic self-efficacy, Reflective thinking, Problem-solving, Metacognitive awareness, flipped learning environment, feedback

References

• Agudo-Peregrina, A. F., Iglesias-Pradas, S., Conde-Gonzalez, M. A., & Hernandez-Garcıa, A. (2014). Can we predict success from log data in VLEs? Classification of interactions for learning analytics and their relation with performance in VLE-supported F2F and online learning. Computers in Human Behavior, 31, 542-550.
• Akın, A., Abacı, R., & Çetin, B. (2007). The validity and reliability study of the Turkish version of the Metacognitive Awareness Inventory. Educational Science: Theory & Practice, 7(2), 655-680.
• Arbaugh, J.B., Cleveland-Innes, M., Diaz, S.R., Garrison, D.R., Ice, P., Richardson, J.C., & Swan, K.P. (2008). Developing a community of inquiry instrument: Testing a measure of the Community of Inquiry framework using a multi-institutional sample. The Internet and Higher Education, 11(3-4), 133-136.
• Avella, J. T., Kebritchi, M., Nunn, S. G., & Kanai, T. (2016). Learning analytics methods, benefits, and challenges in higher education: A systematic literature review. Online Learning, 20(2), 13-29.
• Creswell, J. W. (2003). Qualitative, quantitative and mixed methods approaches. Thousand Oaks, CA: Sage Publications.
• Çiğdem, H., & Kurt, A. A. (2012). Yansıtıcı düşünme ölçeğinin Türkçeye uyarlanması. Uludağ Üniversitesi Eğitim Fakültesi Dergisi, 25(2), 475-493.
• Dietz-Uhler, B., & Hurn, J. E. (2013). Using learning analytics to predict (and improve) student success: A faculty perspective. Journal of Interactive Online Learning, 12(1), 17-26.
• Dunnam, M.V. (2018). Correlational study examining graduate students online interactions and academic achievement using learning analytics. Doctoral Dissertation, Grand Canyon University.
• Ekici, G. (2012). Academic self-efficacy scale: the study of adaptation to Turkish, validity and reliability. Hacettepe Unıversity Journal of Education, 43, 174-185.
• Gasevic, D., Dawson, S., Rogers, T., & Gasevic, D. (2016). Learning analytics should not promote one size fits all: The effects of instructional conditions in predicting academic success. The Internet and Higher Education, 28, 68-84.
• Heppner, P. P., & Petersen, C. H. (1982). The development and implications of a personal problem-solving inventory. Journal of Counseling Psychology, 29(1), 66-75.
• Karaoglan Yilmaz, F. G. (2017). Predictors of community of inquiry in a flipped classroom model. Journal of Educational Technology Systems, 46(1), 87-102.
• Kember, D., Leung, D. Y., Jones, A., Loke, A. Y., McKay, J., Sinclair, K., ... & Yeung, E. (2000). Development of a questionnaire to measure the level of reflective thinking. Assessment & Evaluation in Higher Education, 25(4), 381-395.
• Kentnor, H. E. (2015). Distance education and the evolution of online learning in the United States. Curriculum and Teaching Dialogue, 17(1), 21-34.
• Kim, Y. H. (2014). Learning motivations, academic self-efficacy, and problem-solving processes after practice education evaluation. Journal of the Korea Academia-Industrial Cooperation Society, 15(10), 6176-6186.
• Ma, J., Han, X., Yang, J., & Cheng, J. (2015). Examining the necessary condition for engagement in an online learning environment based on learning analytics approach: The role of the instructor. The Internet and Higher Education, 24, 26-34.
• Marks, A., Al-Ali, M., & Rietsema, K. (2016). Learning management systems: A shift toward learning and academic analytics. International Journal of Emerging Technologies in Learning, 11(4), 77-82.
• Owen, S. V., & Froman, R. D. (1988). Development of a college academic self-efficacy scale. Paper presented at the Annual Meeting of the National Council on Measurement in Education (New Orleans, LA, April 6- 8).
• Öztürk, E. (2012). An adaptation of the community of inquiry index: The study of validity and reliability. Elementary Education Online, 11(2), 408-422.
• Phan, H. P. (2007). An examination of reflective thinking, learning approaches, and self‐efficacy beliefs at the University of the South Pacific: A path analysis approach. Educational Psychology, 27(6), 789-806.
• Phan, H. P. (2014). Self-efficacy, reflection, and achievement: A short-term longitudinal examination. The Journal of Educational Research, 107(2), 90-102.
• Sahin, N., Sahin, N. H., & Heppner, P. P. (1993). Psychometric properties of the problem-solving inventory in a group of Turkish university students. Cognitive Therapy and Research, 17(4), 379-396.
• Schraw, G., & Sperling-Dennison, R. (1994). Assessing metacognitive awareness. Contemporary Educational Psychology, 19, 460-470.
• Shea, P., & Bidjerano, T. (2010). Learning presence: Towards a theory of self-efficacy, self-regulation, and the development of a communities of inquiry in online and blended learning environments. Computers & Education, 55(4), 1721-1731.
• Siemens, G., Dawson, S., & Lynch, G. (2013). Improving the quality and productivity of the higher education sector. Policy and Strategy for Systems-Level Deployment of Learning Analytics. Canberra, Australia: Society for Learning Analytics Research for the Australian Office for Learning and Teaching.