An Investigation into Pre-Service Teachers’ Self-Regulated Online Learning Perceptions

Year 2021, , 143 - 159, 30.06.2021

Atilla Özdemir , Ahmet Önal

https://doi.org/10.33200/ijcer.865189

Cited By: 3

Abstract

The sudden outbreak of Covid-19 pandemics has accelerated the process of technological transformation experienced by educational institutions. Traditional face-to-face education has been replaced by distance education as a precaution to minimize physical contact among the teachers and the students. As a result, self-regulatory skills have become a more significant factor for academic success in distance education. In line with this, the main aim of the study is to identify the level of self-regulated online learning perceptions of pre-service teachers in Turkish higher education context. Furthermore, the study also aims to reveal whether the pre-service teachers’ self-regulated online learning perceptions differ according to several variables such as their gender, department, class, level of digital literacy and the time they daily spend online. A total of 353 pre-service teachers from various departments have participated in the study. The findings of the study indicate that pre-service teachers’ perceptions of self-regulated online learning are at moderate level; thus, it can be argued that the level of their perceptions is far from satisfactory. Additionally, it has been observed that the pre-service teachers’ self-regulated online learning perceptions differ according to their gender, level of digital literacy and time daily spent online. To be more precise, it has been noted that self-regulated online learning perceptions of female pre-service teachers are higher than those of male pre-service teachers. In a similar vein, it has been observed that the higher the level of digital literacy and the more time spent online, the higher the self-regulated online learning perceptions of pre-service teachers. The overall results of the study suggest that self-regulation skills of the pre-service teachers need to be emphasized and improved with the aim of contributing to the efficient implementation of distance education.

Keywords

Self-regulation, Self-regulated online learning, Distance education

References

• Ally, M. (2008). Foundations of educational theory for online learning. In T. Anderson (Ed.), Theory and practice of online learning (2nd Ed.), 3-31.
• Arias Soto, L. & González Gutiérrez, Y. (2019). Digital literacy and basic self-regulation habits in early childhood learning of English as a Foreign Language. Folios, 49, 177–196.
• Bayrak, M., Aydemir, M. & Karaman, S. (2017). An investigation of the learning styles and the satisfaction levels of the distance education students. Çukurova Üniversitesi Eğitim Fakültesi Dergisi, 46(1), 231-263.
• Boettcher, J.V. & Conrad, R.M. (2016). The online teaching survival guide (2nd ed.). John Wiley & Sons.
• Boz Yüksekdağ, B. (2016). Açık ve uzaktan eğitimde öğrenme. AUAd, 2(4), 127-138.
• Büyüköztürk, Y. (2002). Faktör analizi: Temel kavramlar ve ölçek geliştirmede kullanımı, Kuram ve Uygulamada Eğitim Yönetimi. 32 (32), 470-483. Retrieved from https://dergipark.org.tr/tr/pub/kuey/issue/10365/126871.
• Cho, M. H. & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290-301. https://doi.org/10.1080/01587919.2013.835770
• Davis, T. L. (1995). Gender differences in masking negative emotions: Ability or motivation?. Develop. Psychol., 31, 660-667.
• Demirbag, M. & Bahcivan, E. (2021). Comprehensive exploration of digital literacy: Embedded with self‑regulation and epistemological beliefs. Journal of Science Education and Technology, (2021). https://doi.org/10.1007/s10956-020-09887-9.
• Devran, Y. & Elitaş, T. (2016). Uzaktan eğitim: fırsatlar ve tehditler. AJIT-e: Online Academic Journal of Information Technology, 8(27), 31-40.
• Dörnyei, Z. (2007). Research methods in applied linguistics: Quantitative, qualitative, and mixed methodologies. OUP.
• Eby, G. (2013). Uzaktan eğitim (UZE) ortamlarının tasarımı: Yazılım mühendisliği yaşam döngüsü yaklaşımı. Kültür.
• Eygü, H. & Karaman, S. (2013). Uzaktan eğitim öğrencilerinin memnuniyet algıları üzerine bir araştırma. Kırıkkale Üniversitesi Sosyal Bilimler Dergisi, 3(1), 36–59.
• Fan, X. Thompson, B. & Wang, L. (1999). Effects of sample size, estimation methods, and model specification on structural equation modeling fit indexes. Structural Equation Modeling, 6(1), 56-83.
• Field, A. (2009). Discovering statistics using SPSS (and sex and drugs and rock ‘n’ roll) (3rd Ed.). SAGE Publications Ltd.
• Gestsdottir, S., von Suchodoletz, A., Wanless, S. B., Hubert, B., Guimard, P., Birgisdottir, F, Gunzenhauser, C. & McClelland, M. (2014). Early behavioral self-regulation, academic achievement, and gender: Longitudinal findings from France, Germany, and Iceland. Applied Developmental Science, 18, 90-109. https://doi.org/10.1080/10888691.2014.894870
• Hadwin, A., Järvelä, S. & Miller, M. (2017). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 83-106. Routledge Taylor & Francis Group.
• Hartnett, M. (2016). Motivation in online education. Springer.
• Hattie, J. (2009). Visible learning: A synthesis of over 800 meta-analyses relating to achievement. Routledge.
• Henson, R. K. & Roberts, J. K. (2006). Exploratory factor analysis in published research: Common errors and some comment on improved practice. Educational and Psychological Measurement, 66(3), 393-416.
• Howitt, D. & Cramer, D. (2011). Introduction to SPSS statistics in psychology: For version 19 and earlier (5th Ed.). Pearson Education Limited.
• Hoyle, R. H. & Dent A. L. (2017). Developmental trajectories of skills and abilities relevant for self-regulation of learning and performance. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 49-63. Routledge Taylor & Francis Group.
• Hu, L. T. & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6(1), 1–55. https://doi.org/10.1080/10705519909540118
• Jansen, R., Van Leeuwen, A., Janssen, J. & Kester, L. (2018). Validation of the revised self-regulated online learning questionnaire. In V. Pammer-Schindler, M. Pérez-Sanagustín, H. Drachsler, R. Elferink, & M. Scheffel (Eds.), Lifelong Technology-Enhanced Learning. EC-TEL 2018.: Lecture Notes in Computer Science, (11082), https://doi.org/10.1007/978-3-319-98572-5_9.
• Jansen, R.S., van Leeuwen, A., Janssen, J., Kester, L. & Kalz, M. (2017). Validation of the self-regulated online learning questionnaire. J. Comput. High. Educ., 29, 6–27.
• Järvenoja, H., Järvelä, S. & Malmberg, J. (2015). Understanding regulated learning in situative and contextual frameworks. Educational Psychologist, 50 (3), 204-219.
• Jöreskog, K.G. & Sörbom, D. (1993). Lisrel 8: Structural equation modelling with simplis command language. Scientific Software International Inc.
• Karasar, N. (2000). Bilimsel araştırma yöntemi (10. Baskı). Nobel Yayın Dağıtım.
• Karasu, G. & Sarı, Y. E. (2019). Uzaktan eğitim ve yabancı dil öğrenme özerkliği. Diyalog, 2019(2), 321-334.
• Kavrat, B. & Türel, Y. K. (2013). Çevrimiçi uzaktan eğitimde öğretmen rollerini ve yeterliliklerini belirleme ölçeği geliştirme. The Journal of Instructional Technologies &Teacher Education, 1(3), 23-33.
• Kışla, T. (2016). Uzaktan eğitime yönelik tutum ölçeği geliştirme çalışması. Ege Eğitim Dergisi, 2016(17), 258-271.
• Kirk, R. E. (2008). Statistics an introduction (5th Ed.). Thomson Higher Education.
• Koç, E. (2020). An evaluation of distance learning in higher education through the eyes of course instructors. Akdeniz Üniversitesi Eğitim Fakültesi Dergisi, 3(1), 25-39.
• Kramarski, B. (2017). Teachers as agents in promoting students’ srl and performance. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 223-239. Routledge Taylor & Francis Group.
• Krusekopf, C. (2019). Internationalisation of online learning: A double degree model. In Altmann, A., Ebersberger, B., Mössenlechner, C. & Wieser, D. (Eds). The disruptive power of online education: Challenges, opportunities, responses, 63-80. Emerald Publishing.
• Marsh, H. W. & Hocevar, D. (1988). A new, more powerful approach to multitrait-multimethod analyses: Application of second-order confirmatory factor analysis. Journal of Applied Psychology, 73(1), 107–117. https://doi.org/10.1037/0021-9010.73.1.107
• McAvinia, C. (2016). Online learning and its users: Lessons for higher education. Chandos Publishing/Elsevier.
• McInerney, D. M. & King, R. B. (2017). Culture and self-regulation in educational contexts. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 485-502. Routledge Taylor & Francis Group.
• McKillup, S. (2012). Statistics explained: An introductory guide for life scientists (2nd Ed.). Cambridge University Press.
• Meece, J. L. & Painter, J. (2008). Gender, self-regulation, and motivation. In Schunk, D. H. & Zimmerman, B. J. (Eds.). Motivation and self-regulated learning: Theory, research, and applications, 339-367. Lawrence Erlbaum Associates Publishers.
• Mertler, C. A. & Vannatta, R. A. (2005). Advanced and multivariate statistical methods: Practical application and interpretation (3rd Ed.). Pyrczak Publishing.
• Mevarech, Z. R., Verschaffel, L. & De Corte, E. (2017). Metacognitive pedagogies in mathematics classrooms. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 109-123. Routledge Taylor & Francis Group.
• Moore, M. & Kearsley, G. (2011). Distance education: A systems view of online learning. Wadsworth Publishing.
• Nunan, D. (1992). Research methods in language learning. Cambridge University Press.
• Obexer, R. (2019). Scaling online learning: The case for a programme-level approach. In Altmann, A., Ebersberger, B., Mössenlechner, C. & Wieser, D. (Eds). The disruptive power of online education: Challenges, opportunities, responses, 7-26. Emerald Publishing.
• Özgür, H. (2013). Learning styles of distance education students: Trakya university sample. Trakya University Journal of Education, 3(2), 85-91.
• Pagano, R. R. (2009). Understanding statistics in the behavioral sciences (9th Ed.). Wadsworth, Cengage Learning.
• Perry, N. E., Hutchinson, L. & Thauberger, C. (2008). Talking about teaching self-regulated learning: Scaffolding student teachers’ development and use of practices that promote self-regulated learning. International Journal of Educational Research, 47 (2), 97-108.
• Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation, 451-502. Academic.
• Puustinen, M. & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45(3), 269-286.
• Schmitt, T. A. (2011). Current methodological considerations in exploratory and confirmatory factor analysis. Journal of Psychoeducational Assessment, 29, 304-321.
• Simonson, M., Smaldino, S. & Zvacek, S. (2015). Teaching and learning at a distance: Foundations of distance education (6th ed.). IAP.
• Sinatra, G. M. & Taasoobshirazi, G. (2017). The self-regulation of learning and conceptual change in science. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 153-165. Routledge Taylor & Francis Group.
• Stevens, J. P. (2002). Applied multivariate statistics for the social sciences. Lawrance Erlbaum Association, Inc.
• Stevens, J. P. (2009). Applied multivariate statistics for the social sciences (5th Ed.). Taylor and Francis Group, LLC.
• Tabachnick, B. G. & Fidel, L. S. (2014). Using multivariate statistics (6th Ed.). Pearson Education Limited.
• Thiede, K. W. & de Bruin, A. B. H. (2017). Self-regulated learning in reading. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 124-137. Routledge Taylor & Francis Group.
• Thode, H. C. (2002). Testing for normality. Marcel Dekker, Inc.
• Tseng, W.T., Liu, H. & Nix, J.M.L. (2017). Self-regulation in language learning: Scale validation and gender effects. Perceptual & Motor Skills, 124(2), 531-548.
• Usher, E. L. & Shunk, D. H. (2017). Social cognitive theoretical perspective of self-regulation. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 19-35. Routledge Taylor & Francis Group.
• Wang, L. (2019). Research on assessment of online foreign language teaching system. Advances in Social Science, Education and Humanities Research, 357, 117-120.
• Weis, M., Heikamp, T. & Trommsdorrf, G. (2013). Gender differences in school achievement: The role of self-regulation. Frontiers in Psychology, 4 (442), 1-10.
• Wilcox, R. (2012). Comparing two independent groups via a quantile generalization of the wilcoxon-mann-whitney test. Journal of Modern Applied Statistical Methods, 11, 296-302.
• Winne, P. H. (2017). Cognition and metacognition within self-regulated learning. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 36-48. Routledge Taylor & Francis Group.
• Wolters, C. A. & Won, S. (2017). Validity and the use of self-report questionnaires to assess self-regulated learning. In Shunk, D. H. & Greene, J. A. (Eds). Handbook of self-regulation of learning and performance (2nd Ed.), 307-322. Routledge Taylor & Francis Group.
• Zimmerman, B. J. (2000). Attaining self-regulation. In Boekaerts, M., Pintrich, P. R. & Zeidner, M. (Eds). Handbook of self-regulation, 13-39. Academic Press.
• Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45(1), 166-183.