Structural Equation Modelling Analysis of the Relationships Among University Students' Online Self-Regulation Skills, Satisfaction and Perceived Learning

Year 2022, Volume: 9 Issue: 3, 1 - 22, 01.05.2022

Yakup Yılmaz

https://doi.org/10.17275/per.22.51.9.3

Cited By: 1

Abstract

Teaching-learning activities carried out face-to-face in physical classrooms in higher education have been moved to the online environment due to the Covid-19 pandemic obligation. It is obvious that students' learning experiences and perceptions need to be researched empirically in order to optimize higher education strategies that have been moved to the online environment. Data were collected from 451 students studying in different departments in two education faculties in order to reveal the relationship between their satisfaction in the e-learning environment and their perceived learning experiences and using online self-regulation strategies based on the autonomous movement of students in the online environment. Descriptive analyses and path analysis were applied in order to answer the proposed research questions. As a result of this structural equation modeling, a relationship was determined between online self-regulation skills, goal setting and help seeking sub-factors, and satisfaction, goal setting, task strategies and self-evaluation sub-factors and perceived learning. In addition, a direct relationship was determined between satisfaction and perceived learning, supporting previous studies. With this research, it is thought that higher education institutions, administrators and instructors carrying out online teaching and learning activities will provide new perspectives on satisfaction and perceived learning outcomes when students' self-control skills are supported.

Keywords

Online self-regulation, satisfaction, perceived learning

References

• Alavi, M., Marakas, G. M. & Youngjin, Y. (2002). A comparative study of distributed learning environments on learning outcomes. Information Systems Research, 13, 404–415.
• Albayrak, E., Güngören, Ö. C. & Horzum, M. B. (2014). Adaptation of perceived learning scale to Turkish. Ondokuz Mayıs University Faculty of Education Journal, 33 (1), 1-14.
• Aldholay, A. Abdullah, Z., Isaac, O. & Mutahar, A.M. (2019). Perspective of Yemeni students on use of online learning: Extending the information systems success model with transformational leadership and compatibility. Information Technology and People, 33, 106–128.
• Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40 (1), 133-148.
• Artino, A. R. (2007). Online military training: using a social cognitive view of motivation and self-regulation to understand students' satisfaction, perceived learning, and choice. Quarterly Review of Distance Education, 8 (3), 191-202.
• Artino, A.R. & Stephens, J.M. (2009). Academic motivation and self-regulation: A comparative analysis of undergraduate and graduate students learning online. The Internet and Higher Education, 12(3), 146-151.
• Azevedo, R., & Cromley, J. G. (2004). Does training on self-regulated learning facilitate students' learning with hypermedia? Journal of Educational Psychology, 96(3), 523–535.
• Azevedo, R., Guthrie, J. T. & Seibert, D. (2004). The role of self-regulated learning in fostering students’ conceptual understanding of complex systems with hypermedia. Journal of Educational Computing Research, 30, 87–111.
• Baber, H. (2020). Determinants of students’ perceived learning outcome and satisfaction in online learning during the pandemic of COVID-19. Journal of Education and e-Learning Research, 7 (3), 285-292.
• Bandura, A. (1986). Social foundations of thought and action. Englewood Cliffs, NJ: Prentice Hall.
• Bao, R. (2020) Collaborative dialogue between complete beginners of Chinese as a foreign language: Implications it has for Chinese language teaching and learning. The Language Learning Journal, 48(4), 414-426.
• Barnard, L., Lan, W. Y., To, Y. M., Paton, V. O. & Lai, S. L. (2009). Measuring self-regulation in online and blended learning environments. The Internet and Higher Education, 12(1), 1-6.
• Baturay, M. H. (2011). Relationships among sense of classroom community, perceived cognitive learning and satisfaction of students at an e-learning course. Interactive Learning Environments, 19(5), 563–575.
• Beach, P. (2017). Self-directed online learning: A theoretical model for understanding elementary teachers' online learning experiences. Teaching and Teacher Education, 61, 60-72.
• Blackburn, H. A. (2014). A mixed methods study: Assessing and understanding technology pedagogy and content knowledge among college level teaching faculty (Order No. 3629463). ProQuest Dissertations & Theses Global
• Bradley, R. L., Browne, B. L. & Kelley, H. M. (2017). Examining the influence of self-efficacy and self-regulation in online learning. College Student Journal, 51(4), 518–530.
• Burd, B.A. & Buchanan, L.E. (2004). Teaching the teachers: Teaching and learning online. Reference Services Review,32, 404–412.
• Çalışkan, S. & Selçuk, S. G. (2010). Pre-service teachers use of self-regulation strategies in physics problem solving: Effects of gender and academic achievement. International Journal of Physical Sciences, 5 (12), 1926-1938.
• Carrillo, C. & Flores, M. A. (2020). COVID-19 and teacher education: a literature review of online teaching and learning practices. European Journal of Teacher Education, 43(4), 466-487.
• Caspi, A., & Blau, I. (2008). Social presence in online discussion groups: Testing three conceptions and their relations to perceived learning. Social Psychology of Education, 11, 323-346.
• Chiu, Y.L., Liang, J.C. & Tsai, C.C. (2013). Internet-specific epistemic beliefs and self-regulated learning in online academic information searching. Metacognition Learning, 8, 235–260.
• Cho, M-H. & Kim, B. J. (2013). Students' self-regulation for interaction with others in online learning environments. The Internet and Higher Education, 17, 69-75.
• Chu, R. J. & Chu, A. Z. (2010). Multi-level analysis of peer support, Internet self-efficacy and e-learning outcomes: The contextual effects of collectivism and group potency. Computer & Education, 55, 145–154.
• Creswell, J. W. (2008). Educational research: Planning, conducting and evaluating quantitative and qualitative research. Upper Saddle River, NJ: Pearson.
• Curtis, M. Z., Bonk, C. J. & Doo, M. Y. (2020). Self directed learning in MOOCs: Exploring the relationships among motivation, self monitoring, and self management. Education Technology Research Development, 68, 2073-2093.
• Ejubovi, A. & Puska, A. (2019). Impact of self-regulated learning on academic performance and satisfaction of students in the online environment. Knowledge Management & E-Learning, 11(3), 345–363.
• Eom, S. B. & Ashill, N. (2016). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An update. Decision Sciences Journal of Innovative Education, 14(2), 185-215.
• Flores, M. A. & Gago, M. (2020). Teacher education in timesof COVID-19 pandemic in Portugal: national, institutional and pedagogical responses. Journal of Education for Teaching, 46(4), 507-516.
• Frankel, J.R., Wallen, N.,E. & Hyun, H.H. (2012). How to Design and Evaluate Research in Education. McGraw-Hill, NY.
• Fredericksen, E., Pickett, A., Shea, P., Pelz, W. & Swan, K. (2000). Student satisfaction and perceived learning with on-line courses: Principles and examples from the SUNY learning network. Journal of Asynchronous Learning Networks, 4(2), 7–41.
• Gray, J.A. & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. International Journal of Educational Leadership Preparation, 11 (1), 1-20.
• Gülbahar, Y. (2012). Study of developing scales for assessment of the levels of readiness and satisfaction of participants in e learning environments. Ankara University Journal of Faculty of Educational Sciences, 45 (2), 119-138.
• Hofmeister, C. & Pilz, M. (2020). Using e-learning to deliver in-service teacher training in the vocational education sector: perception and acceptance in Poland, Italy and Germany. Education Sciences, 10, 182.
• Horzum, M. B. (2007). Web based new instructional technologies: Web 2.0 tools. Education Sciences and Practices, 6 (12), 99-121.
• Horzum, M.B., Kaymak, Z.D. & Güngören, Ö.C. (2015). Structural equation modeling towards online learning readiness. academic motivations, and perceived learning. Educational Sciences: Theory and Practice, 15 (3), 759-770.
• Hurd, S. (2006). Towards a better understanding of the dynamic role of the distance language learner: learner perceptions of personality, motivation, roles, and approaches. Distance Education, 27(3), 303-329.
• Ilgaz, H. & Gülbahar, Y. (2015). A snapshot of online learners e-readiness e-satisfaction and expectations. International Review of Research in Open and Distributed Learning, 16(2), 171–187.
• İnan, F., Yükseltürk, E., Kuruçay, M. & Flores, R. (2016). The impact of self-regulation strategies on student success and satisfaction in an online course. International Journal on E-Learning, 16 (1), 23-32.
• Johannes König, Daniela J. Jäger-Biela & Nina Glutsch (2020) Adapting toonline teaching during COVID-19 school closure: Teacher education and teacher competence effects among early career teachers in Germany. European Journal of Teacher Education, 43(4), 608-622.
• Kang, M. & Im, I. (2013). Factors of learner–instructor interaction which predict perceived learning outcomes in online learning environment. Journal of Computer Assisted Learning, 29 (3), 292-301.
• Kara, M., Kukul, V. & Çakır, R. (2021). Self-regulation in three types of online interaction: how does it predict online pre-service teachers perceived learning and satisfaction? The Asia-Pacific Education Researcher 30, 1–10.
• Karataş, S. (2005). Comparisons of internet-based and face-to-face learning systems based on 'equivalency of experiences' according to students' academic achievements and satisfactions. Unpublished doctoral dissertation, Ankara University, Ankara.
• Karataş, K. & Arpaci, İ. (2021). The role of self-directed learning, metacognition, and 21st century skills predicting the readiness for online learning. Contemporary Educational Technology, 13 (3), 1-13.
• Kilis, S. & Yıldırım, Z. (2018). Online self-regulation questionnaire: validity and reliability study of Turkish translation. Cukurova University Faculty of Education Journal, 47 (1), 233-245.
• Kline, R. B. (2011). Principles and practice of structural equation modeling. New York, NY: Guilford Press.
• Kuo, Y. C. (2010). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in distance education courses (Order No: 3419203). Proquest Dissertations & Theses Global.
• Kuo, Y. C., Walker, A. E., Schroder, K. E. E. & Belland, B. R. (2014). Interaction, internet self-efficacy, and self-regulated learning as predictors of student satisfaction in online education courses. The Internet and Higher Education,20, 35–50.
• Lai, C. & Gu M. (2011). Self-regulated out-of-class language learning with technology. Computer Assisted Language Learning, 24(4), 317-335.
• Landrum, B. (2020). Examining students’ confidence to learn online, self-regulation skills and perceptions of satisfaction and usefulness of online classes. Online Learning, 24(3), 128-146.
• Lazowski, R. A. & Hulleman, C. S. (2016). Motivation interventions in education: A meta-analytic review. Review of Educational Research, 86 (2), 602-640.
• Lee, S.W.Y. & Tsai, C.C. (2011). Students’ perceptions of collaboration, self-regulated learning, and information seeking in the context of Internet-based learning and traditional learning. Computers in Human Behavior, 27 (2), 905-914.
• Liao, P. W. & Hsieh, J. Y. (2011). What influences Internet-based learning? Social Behavior and Personality, 39(7), 887–896.
• Luka, I. (2018). Summative evaluation of online language learning course efficiency for students studying tourismand hospitality management. Quality Assurance in Education, 26, 446–465
• Martin, F., Stamper, B. & Flowers, C. (2020). Examining student perception of their readiness for online learning: Importance and confidence. Online Learning, 24(2), 38-58.
• Martin, S. & Valdivia, I. M. A. (2017). Students’feedback beliefs and anxiety inonline foreign language oral tasks. International Journal of Educational Technology in Higher Education, 14 (18), 1-15.
• Matthews, J. S., Ponitz, C. C. & Morrison, F. J. (2009). Early gender differences in self-regulation and academic achievement. Journal of Educational Psychology, 101(3), 689–704.
• Moore, M. G. (1997). Theory of transactional distance. D. Keegan (Ed.), Theoretical Principles of Distance Education inside (s. 22-38). Routledge.
• Moore, J. C. (2005). The Sloan consortium quality framework and the five pillars. Newburyport, MA: The Sloan Consortium.
• Niemi, H., Nevgi, A. & Virtanen, P. (2003). Towards self-regulation in web-based learning. Journal of Educational Media, 28(1), 49–71.
• Pasch, G. & Stewart, Q. (2020). Using the Internet to teach the Internet: An opportunistic approach. The Electronic Library, 20, 401–412.
• Pérez-Pérez,M., Serrano-Bedia, A. M. & García-Piqueres, G. (2019). An analysis of factors affecting students’ perceptions of learning outcomes with Moodle. Journal of Further and Higher Education, 44(8), 1114–1129.
• Piccoli, G., Ahmad, R. & Ives,B. (2001). Web-based virtual learning environments: A research framework and a preliminary assessment of effectiveness in basic it skills training. Management Information Systems Quarterly, 25 (4), 401-427.
• Pintrich, P. R. (2000). The role of goal orientation in self-regulated learning. Handbook of Self-Regulation, 451–502.
• Porat, E., Blau, I. & Barak, A. (2018). Measuring digital literacies: junior high-school students' perceived competencies versus actual performance. Computers & Education, 126, pp.23–36.
• Puustinen, M. & Pulkkinen, L. (2001). Models of self-regulated learning: A review. Scandinavian Journal of Educational Research, 45(3), 269-286.
• Quezada, L. R., Talbot, C. & Quezada-Parker, K.B. (2020). From bricks and mortar to remote teaching: a teacher education program‘s response to COVID-19. Journal of Education for Teaching, 46(4), 472-483.
• Ranalli, J. (2012). Alternative models of self-regulation and implications for L2 strategy research. Studies in Self-Access Learning Journal, 3(4), 357-376.
• Sakal, M. (2017). Investigation on the demographic characteristics of readiness levels of online educated students. Journal Of Social Sciences And Humanities Researches, 18 (39), 81-102.
• Schunk, D. H. (2012). Social cognitive theory. In K. R. Harris, S. Graham, & T. Urdan (Eds.), Educational psychology handbook: Vol. 1. Theories, constructs, and critical issues (pp. 101–123). Washington, DC: American Psychological Association.
• Schwinger, M. & Otterpohl, N. (2017). Which one works best? Considering the relative importance of motivational regulation strategies. Learning and Individual Differences, 53, 122-132.
• Shevlin, M. & Miles, J. N. (1998). Effects of sample size, model specification and factor loadings on the GFI in confirmatory factor analysis. Personality and Individual Differences, 25, 85–90.
• Strømsø, H.I. & Bråten, I. (2010). The role of personal epistemology in the self-regulation of internet-based learning. Metacognition Learning, 5, 91–111.
• Su, Y., Zheng, C., Liang, J-C. & Tsai, C-C. (2018). Examining the relationship between English language learners’ online self-regulation and their self-efficacy. Australasian Journal of Educational Technology, 34 (3), 105-122.
• Ünal, N., Şanlıer, N. & Şengil, A. Z. (2020). Evaluation of the university students' readiness for online learning and the experiences related to distance education during the pandemic period. Journal of Inonu University Health Services Vocational School, 9 (1), 89-104.
• Üredi, I. & Üredi, L. (2007). Creating a learning environment that develops students' self-regulation skills. Yeditepe University Journal of Education Faculty, 2(2), 27–39.
• Teng, L.S. & Zhang, L.J. (2016). A questionnaire-based validation of multidimensional models of selfregulated learning strategies. The Modern Language Journal, 100 (3), 674-701.
• Teo, T. & Wong, S. L. (2013). Modeling key divers of e-learning satisfaction among student teacher. Journal Educational Computing Research, 48 (1), 71-95.
• Turhangil-Erenler HH. (2019). A structural equation model to evaluate students’ learning and satisfaction. Computer Application in Engineering Education, 28 (2), 254–267.
• Vanslambroucka,S., Zhua, C., Pynooa, B., Lombaertsa, K. & Tondeurb, J. (2019). A latent profile analysis of adult students’ online self-regulation in blended learning environments. Compters in Behavior Human, 99, 126-136.
• Vanslambroucka, S., Zhua, G., Pynooa, B., Thomasa, V., Lombaertsa,K. & Tondeurb, J. (2019). An in-depth analysis of adult students in blended environments: Do they regulate their learning in an “old schools” way? Computer & Education, 128, 57-87.
• Virtanen, M. A., Kääriäinen, M., Liikanen, E. & Haavisto, E. (2017). The comparison of students’ satisfaction between ubiquitous and web-basedlearning environments. Education and Information Technologies, 22, 2565–2581.
• Wang, C.-H., Shannon, D. M., & Ross, M. E. (2013). Students' characteristics, self-regulated learning, technology self-efficacy, and course outcomes in online learning. Distance Education, 34, 302–323.
• Wilde, N. & Hsu, A. (2019). The influence of general self-efficacy on the interpretation of vicarious experience information within online learning. International Journal of Educational Technology in Higher Education, 16 (26), 1-20.
• Winters, F.I., Greene, J.A. & Costich, C. M. (2008). Self-regulation of learning within computer-based learning environments: a critical analysis. Educational Psychology Review, 20, 429–444.
• Wua, J.H., Tennyson, R.D. & Hsia, T.L.(2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55, 155-164.
• Yaman, N. & Muhlis, M. (2020). Students' social presence and perceived learning toward ccu course in online classroom (an evaluating of learning process during pandemic Coronavirus). English ad Literature Journal, 7 (1), 61-73.
• Yılmaz, N. A. (2020). Investigation of students' attitudes towards applied distance education in the covid-19 pandemic process in higher education institutions: example of physiotherapy and rehabilitation department. Necmettin Erbakan University Faculty of Health Sciences Journal, 3(1), 15-20.
• Yukselturk, E. & Bulut, S. (2007). Predictors for student success in an online course. Ed¬ucational Technology & Society, 10(2), 71-83.
• Yunusa, A. A. & Umar, I. N. (2020). A scoping review of critical predictive factors (CPFs) of satisfaction and perceived learning outcomes in E-learning environments. Education and Information Technologies, 26, 1223-1270.
• Zheng, C., Liang, J.C., Li, M. & Tsai, C.C. (2018). The relationship between English language learners’ motivation and online self-regulation: A structural equation modelling approach. System, 76, 144-157.
• Zhou, X., Chai, C. S., Jong, M. S. Y. & Xiong, X. B. (2021). Does relatedness matter for online self-regulated learning to promote perceived learning gains and satisfaction? Asia-Pacific Education Research, 30 (3), 205-215.
• Zhu, C. (2019). Self-efficacy and self-esteem in online learning environments of adult learners. International Journal of Learning Technology, 14 (1), 4-17.
• Zimmerman, B. J. (1994). Dimensions of academic self-regulation: a conceptual framework for education. In D.H. Schunk and B. J. Zimmerman (Eds.). Self-Regulation ofLearning and Performance (pp. 3-24). New Jersey: Lawrence Erlbaum Associates Publishers.
• Zimmerman, B.J. & Kitsantas, A. (2014). Comparing students’ self-discipline and self-regulation measuresand their prediction of academic achievement. Contemporary Educational Psychology, 39, 145-155.
• Zimmerman, B.J. & Risemberg, R. (1997). Caveats and recommendations about self-regulation of writing: a social cognitive rejoinder. Contemporary Educational Psychology, 22, 115-122.
• Zimmerman, B. J. & Schunk, D. H. (Eds.). (2011). Handbook of self-regulation of learning and performance. Routledge/Taylor & Francis Group.