Educational Technology International

  • 제24권2호
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  • Pages.203-236
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  • 2023
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  • 1598-5776(pISSN)
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  • 2289-0300(eISSN)
한국교육공학회 (The Korean Society for Education Technology)
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A Study on the Relationship Analysis between Online Self-regulated Learning (OSRL), Satisfaction, and Continuous Participation Intention of Online Courses in University

  • 투고 : 2023.09.03
  • 심사 : 2023.10.10
  • 발행 : 2023.10.30
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초록

The purpose of this study is to explore the structural relationship between COVID-19-induced sub-dimensions of Online Self-Regulated Learning (OSRL) and satisfaction in online courses conducted in the 'post-COVID-19 era,' as well as to investigate the moderating effects of situational variables such as 'course planning,' 'device type,' and 'course repetition.' To achieve this, the study constructs a measurement model with sub-dimensions of Environment Structuring, Learning Strategy, Help Seeking, and Self-Evaluation as components of OSRL. Participants in this study were selected from university students who enrolled in online courses offered by the Department of Education at University A in the metropolitan area. The research findings reveal several key insights. First, among the sub-dimensions of Online Self-Regulated Learning, Environment Structuring, Learning Strategy, and Self-Evaluation significantly influence satisfaction with online courses. Second, students' satisfaction with online courses significantly influences their intention to continue participating in such courses. Third, 'course planning' during online course hours and 'course repetition' play a moderating role in the relationship between sub-dimensions of Online Self-Regulated Learning and satisfaction. Based on the discussion of these research results, this study concludes by suggesting some future implications and challenges of online courses.

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참고문헌

  1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179-211. https://doi.org/10.1016/0749-5978(91)90020-T
  2. Alabdullatif, H., & Velazquez-Iturbide, J. A. (2020). Personality traits and intention to continue using massive open online courses (ICM) in Spain: The mediating role of motivations. International Journal of Human-Computer Interaction, 36(20), 1953-1967. https://doi.org/10.1080/10447318.2020.1805873
  3. Anderson, J., & Gerbing, D. (1988). Structural equation modeling in practice: A review and recommended two-step approach. Psychological Bulletin, 103(3), 411-423. http://dx.doi.org/10.1037/0033-2909.103.3.411
  4. Barnard, L., Lan, W., To, Y., Paton, V., & Lai, S. (2009). Measuring self-regulation in online and blended learning environments. Internet and Higher Education, 12, 1-6. https://doi.org/10.1016/j.iheduc.2008.10.005
  5. Cao, J., Lin, M., Crews, J., Burgoon, J., & Nunamaker, J., Jr. (2005). Virtual interaction for effective e-learning. Proceedings of the International Conference on Information Systems, ICIS 2005, USA.
  6. Cardozo, R. N. (1965). An experimental study of customer effort, expectation, and satisfaction. Journal of Marketing Research, 3, 244-249. http://dx.doi.org/10.2307/3150182
  7. Chiu, C. M., Hsu, M. H., Sun, S. Y., Lin, T. C., & Sun, P. C. (2005). Usability, quality, value and e-learning continuance decisions. Computers & Education, 45(4), 399-416. https://doi.org/10.1016/j.compedu.2004.06.001
  8. Cunningham, C., & Billingsley, M. (2003). Curriculum Webs: A practical guide to weaving the Web into teaching and learning. Allyn and Bacon. https://umbrella.lib.umb.edu/discovery/fulldisplay?vid=01MA_UMB:01MA_UMB&tab=Everything&docid=alma993844093503746&context=L&lang=en
  9. Doll, W., Xia, W., & Torkzadeh, G. (1994). A confirmatory factor analysis of the end-user computing satisfaction instrument. MIS Quarterly, 18(4), 453-461. https://doi.org/10.2307/249524
  10. Fornell, C., & Larcker, D. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382-388. https://doi.org/10.2307/3150980
  11. Ha, J., & Jang, S. (2010). Perceived values, satisfaction, and behavioral intentions: The role of familiarity in Korean restaurants. International Journal of Hospitality Management, 29, 2-13. https://doi.org/10.1016/j.ijhm.2009.03.009
  12. Hamdan, K., Al-Bashaireh, A., Zahran, Z., Al-Daghestani, A., Samira, A., & Shaheen, A. (2021). University students' interaction, internet self-efficacy, self-regulation and satisfaction with online education during pandemic crises of COVID-19(SARS-CoV-2). International Journal of Educational Management. https://doi.org/10.1108/IJEM-11-2020-0513.
  13. Han, S., Yoo, H. S., & Ju, D. Y. (2015). Contents experience according to deformation types of smart device. Proceedings of the 2015 Korea Contents Society General Conference. http://www.riss.kr/link?id=A100515505
  14. Hayashi, A., Chen, C., Ryan, T., & Wu, J. (2004). The role of social presence and moderating role of computer self-efficacy in predicting the continuance usage of e-learning systems. Journal of Information Systems Education, 15(2), 139-154. https://aisel.aisnet.org/jise/vol15/iss2/5
  15. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). The difference between emergency remote teaching and online learning. Educause Review, 27. https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
  16. Huber, F., Herrmann, A., & Wricke, M. (2001). Customer satisfaction as an antecedent of price acceptance: Results of an empirical study. Journal of Product & Brand Management, 10(3), 160-169. https://doi.org/10.1108/10610420110395403
  17. Jeong, H. (2022). A study on the relationship analysis between perceived relatedness, online self-regulated learning, perceived learning gains, and satisfaction of non-face-to-face classes in university. Global Creative Leader: Education & Learning, 12(1), 47-73. https://doi.org/10.34226/gcl.2022.12.1.47
  18. Jeong, H., Roh, S. Z., Jung, J. W., & Cho, Y. H. (2020). The challenge of the spread of Covid-19 to education: High quality remote learning for everyone. Journal of Educational Technology, 36(s), 645-669. https://doi.org/10.17232/KSET.36.3.645
  19. Joo, Y. J., & Eun, J. H. (2017). Investigating the structural relationships among service quality, time management behavior, satisfaction and learning persistence in K-MOOC for grades. The Journal of Educational Information and Media, 23(4), 763-788. http://dx.doi.org/10.15833/KAFEIAM.23.4.763
  20. Ju, R. (2020). In the context of COVID-19, a comparison of content quality according to universities' overall distance learning and the effect of content quality, service quality on students' satisfaction. Journal of Educational Technology, 36(s), 931-956. https://doi.org/10.17232/KSET.36.3.931
  21. Kim, H. J. (2021). Digital transformation of education brought by COVID-19 pandemic. Journal of the Korea Society of Computer and Information, 26(6), 183-193. https://doi.org/10.9708/JKSCI.2021.26.06.183
  22. Kim, S., Lim, E., Kim, B., & Lee, Y. (2021). An analysis of learner's experience in distance education at a university in the COVID-19 Situation. The Journal of Educational Information and Media, 27(1), 161-189. https://doi.org/10.15833/KAFEIAM.27.1.161
  23. Kline, R. B. (2005) Principles and practice of structural equation modeling (2nd ed.), Guilford Press. https://www.scirp.org/(S(351jmbntvnsjt1aadkozje))/reference/referencespapers.aspx?referenceid=869389
  24. Kramarski, B., & Gutman, M. (2006). How can self-regulated learning be supported in mathematical E-learning environments? Journal of Computer Assisted Learning Research, 22(1), 24-33. https://doi.org/10.1111/j.1365-2729.2006.00157.x
  25. Kwon, S. (2009). The analysis of differences of learners' participation, procrastination, learning time and achievement by adult learners' adherence of learning time schedule in e-Learning environments. The Journal of Learner-Centered Curriculum and Instruction, 9(3), 61-86. http://www.riss.kr/link?id=A105092039 105092039
  26. Lee, M. (2010). Explaining and predicting users' continuance intention toward e-learning: An extension of the expectation-confirmation model. Computers & Education, 54(2), 506-516. https://doi.org/10.1016/j.compedu.2009.09.002
  27. Lee, S., Park, H., & Sung, E. (2021). Exploration of self-regulated learning variables and learning behavior data affecting academic achievement in an online learning environment. Journal of Korean Association for Educational Information and Media, 27(2), 723-748. https://doi.org/10.15833/KAFEIAM.27.2.723
  28. Liu, X., He, X., Wang, M., & Shen, H. (2022). What influences patients' continuance intention to use AI-powered service robots at hospitals? The role of individual characteristics. Technology in Society, 70, 101996. https://doi.org/10.1016/j.techsoc.2022.101996
  29. Madani, H., Adhikari, A., & Hodgdon, C. (2023). Understanding faculty acceptance of online teaching during the COVID-19 pandemic: A Saudi Arabian case study, Journal of International Education in Business, 16(2), 152-166. https://doi-orgssl.access.ewha.ac.kr/10.1108/JIEB-12-2021-0109
  30. Mahajan, R., Lim, W. M., Kumar, S., & Sareen, M. (2023). COVID-19 and management education: From pandemic to endemic. The International Journal of Management Education, 21(2), 100801. https://doi.org/10.1016/j.ijme.2023.100801
  31. McManus, T. F. (2000). Individualizing instruction in a Web-based hypermedia learning environment: Nonlinearity, advance organizers, and self-regulated learners. Journal of Interactive Learning Research, 11, 219-251. https://www.learntechlib.org/primary/p/8486/
  32. Ministry of Education (2021, February 15). Instruction on the operation of distance learning in general universities (Ministry of Education Ordinance No. 367). https://www.moe.go.kr/boardCnts/viewRenew.do?boardID=72755&lev=0&statusYN=W&s=moe&m=031303&opType=N&boardSeq=88651
  33. Moon, E., & Shin, W. S. (2022). A study on continuous intention of taking online course: focusing on the expectation-confirmation model. The Korean Journal of Educational Methodology Studies, 34(4), 901-922. http://dx.doi.org/10.17927/tkjems.2022.34.4.901
  34. Oliver, R. L., & Swan, J. E. (1989). Consumer perceptions of interpersonal equity and satisfaction in transactions: A field survey approach. Journal of Marketing, 53(2), 21-35. https://doi.org/10.2307/1251411
  35. Park, M., & Heo, G. (2020). A case study on the uncontact-era distance education for personality education : Focused on the role of professors. Journal of Character Education & Research, 5(2), 25-42. https://doi.org/10.46227/JCER.5.2.2
  36. Pintrich, P. R., Smith, D. A., Garcia, T., & McKeachie, W. J. (1993) Reliability and predictive validity of the motivated strategies for learning questionnaire (MSLQ). Educational and Psychological Measurement, 53(3), 801-813. https://doi.org/10.1177/0013164493053003024
  37. Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. The American Journal of Distance Education, 22(2), 72-89. https://doi.org/10.1080/08923640802039024
  38. Richardson, J., & Swan, K. (2003). Examining social presence in online courses in relation to students' perceived learning and satisfaction. Journal of Asynchronous Learning Networks, 7, 68-88 .https://doi.org/10.24059/olj.v7i1.1864
  39. Saxena, C., Baber, H., & Kumar, P. (2021). Examining the moderating effect of perceived benefits of maintaining social distance on e-learning quality during COVID-19 pandemic. Journal of Educational Technology Systems, 49(4), 532-554. https://doi.org/10.1177/0047239520977798
  40. Schraw, G. (2007). The use of computer-based environments for understanding and improving self-regulation. Metacognition Learning, 2, 169-176. https://doi.org/10.1007/s11409-007-9015-8
  41. Schunk, D. H. (2001). Social Cognitive Theory and Self-Regulated Learning. In B. J. Zimmerman & D. H. Schunk (Eds.), Self-Regulated Learning and Academic Achievement: Theoretical Perspectives (pp. 125-151). Lawrence Erlbaum Associates Publishers. https://www.scirp.org/(S(351jmbntv-nsjt1aadkposzje))/reference/referencespapers.aspx?referenceid=3245847
  42. Shin, N. & Chan, J. (2004). Direct and indirect effects of online learning on distance education. British Journal of Education Technology, 55(3), 275-288. https://doi.org/10.1111/j.0007-1013.2004.00389.x
  43. Smarkola, C. (2008). Efficacy of a planned behavior model: Beliefs that contribute to computer usage intentions of student teachers and experienced teachers. Computers in Human Behavior, 24(3), 1196-1215. https://doi.org/10.1016/j.chb.2007.04.005.
  44. Song, D., & Kim, D. (2021). Effects of self-regulation scaffolding on online participation and learning outcomes. Journal of Research on Technology in Education, 53(3), 249-263. https://doi.org/10.1080/15391523.2020.1767525
  45. Steffens, K. (2006). Self-regulated learning in technology-enhanced learning environments: Lessons of a European peer review. European Journal of Education, 41(3), 353-379. http://dx.doi.org/10.1111/j.1465-3435.2006.00271.x
  46. Sung, J., & Kwon, S. (2021). Comparison of students' perceptions of synchronous video conferencing lectures and asynchronous video-recorded lectures: Focusing on students' levels of concentration, understanding, and satisfaction. Journal of Education & Culture, 27(5), 239-267.
  47. Szymanski, D. M., & Hise, R. T. (2000) E-satisfaction: An initial examination. Journal of Retailing, 76, 309-322. https://doi.org/10.1016/S0022-4359(00)00035-X
  48. Taylor, S., & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. https://www.jstor.org/stable/23011007 1007
  49. Tse, D. K., & Wilton, P. C. (1988), Models of consumer satisfaction formation: An extension. Journal of Marketing Research, 25(2), 204-212. http://dx.doi.org/10.2307/3172652
  50. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27, 451-481. http://dx.doi.org/10.1111/j.1540-5915.1996.tb01822.x
  51. 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(3), 302-323. https://doi.org/10.1080/01587919.2013.835779
  52. Ye, J.-H., Lee, Y.-S., Wang, C.-L., Nong, W., Ye, J.-N., & Sun, Y. (2023). The continuous use intention for the online learning of Chinese vocational students in the post-epidemic era: The extended technology acceptance model and expectation confirmation theory. Sustainability, 15, 1819. https://doi.org/10.3390/su15031819
  53. Zhou, X., Chai, C., Jong, M., & Xiong, X. (2021). Does relatedness matter for online self-regulated learning to promote perceived learning gains and satisfaction? Asia-Pacific Education Researcher, 30(3), 205-215. https://doi.org/10.1007/s40299-021-00579-5
  54. Zimmerman, B. J. (2008). Investigating self-regulation and motivation: Historical background, methodological developments, and future prospects. American Educational Research Journal, 45, 166-183. https://doi.org/10.3102/0002831207312909