The influence of online learning interactions on belief in the learning outcomes and self-efficacy of art college students: Evidence from China
List of Authors
  • Deng Yuqing

Keyword
  • Online Learning Interactions, Belief in the Learning Outcomes, Self-Efficacy, Perceived Satisfaction

Abstract
  • Advancements in digital technology, particularly mobile internet, have transformed education systems, making online learning increasingly prevalent. In China, the COVID-19 pandemic accelerated online education adoption, especially in art courses where traditional instruction adapted to virtual platforms. This study examines the relationships among Online Learning Interactions, Perceived Satisfaction, Belief in the Learning Outcomes, and Self-Efficacy among undergraduate art students in Chongqing, China. Using a structural equation model. Findings reveal that online learning interactions significantly enhance students’ belief in the learning outcomes and self-efficacy, with perceived satisfaction mediating some of these relationships. The study extends sociocultural constructivism learning theory by showing how virtual interactions facilitate knowledge construction and boost student confidence. It also supports attribution theory by demonstrating that positive online interactions lead learners to attribute successes to internal factors, enhancing self-efficacy.

Reference
  • 1. Aldholay, A., Isaac, O., Abdullah, Z., Abdulsalam, R., & Al-Shibami, A. H. (2018). An extension of Delone and McLean IS success model with self-efficacy: Online learning usage in Yemen. International Journal of Information and Learning Technology, 35(4), 285–304. https://doi.org/10.1108/IJILT-11-2017-0116 2. Al-Fraihat, D., Joy, M., Masa’deh, R., & Sinclair, J. (2020). Evaluating e-learning systems success: An empirical study. Computers in Human Behavior, 102, 67–86. https://doi.org/10.1016/j.chb.2019.08.004 3. Alqurashi, E. (2016). Self-efficacy in online learning environments: A literature review. Contemporary Issues in Education Research (CIER), 9(1), 45–52. https://doi.org/10.19030/cier.v9i1.9549 4. 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. https://doi.org/10.20448/JOURNAL.509. 2020.73.285.292 5. Baber, H. (2022). Social interaction and effectiveness of online learning: A moderating role of maintaining social distance during the pandemic COVID-19. Asian Education and Development Studies, 11(1), 159–171. https://doi.org/10.1108/AEDS-09-2020-0209 6. Bandura, A., & Walters, R. H. (1977). Social learning theory. Prentice Hall. https://doi.org/10.1016/B978-0-12-813251-7.00057-2 7. Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. International Review of Research in Open and Distance Learning, 9(3). https://doi.org/10.19173/irrodl.v9i3.525 8. Chiodini, J. (2020). Online learning in the time of COVID-19. Travel Medicine and Infectious Disease, 34. https://doi.org/10.1016/j.tmaid.2020.101669 9. Cooper, D. R., & Schindler, P. S. (2014). Business research methods (12th ed.). McGraw-Hill Education. 10. Lima, M., & Zorrilla, M. (2017). Social networks and the building of learning communities: An experimental study of a social MOOC. International Review of Research in Open and Distance Learning, 18(1), 40–64. https://doi.org/10.19173/irrodl.v18i1.2630 11. Diamantopoulos, A., & Siguaw, J. A. (2006). Formative versus reflective indicators in organizational measure development: A comparison and empirical illustration. British Journal of Management, 17(4), 263–282. https://doi.org/10.1111/j.1467-8551.2006.00500.x 12. Ejubović, A., & Puška, A. (2019). Impact of self-regulated learning on academic performance and satisfaction of students in the online environment. Knowledge Management and E-Learning, 11(3), 345–363. https://doi.org/10.34105/j.kmel.2019.11.018 13. 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), n1. 14. Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook. Springer Nature. 15. Henderson, J. B. (2019). Beyond “active learning”: How the ICAP framework permits more acute examination of the popular peer instruction pedagogy. Harvard Educational Review, 89(4), 611–634. https://doi.org/10.17763/1943-5045-89.4.611 16. Hsu, H. C. K., Wang, C. V., & Levesque-Bristol, C. (2019). Reexamining the impact of self-determination theory on learning outcomes in the online learning environment. Education and Information Technologies, 24(3), 2159–2174. https://doi.org/10.1007/ s10639-019-09863-w 17. Irza, Y. (2021). The challenges of online learning during pandemic: Students’ voice. Wanastra: Jurnal Bahasa Dan Sastra, 13(1), 8–13. 18. Katsarou, E., & Chatzipanagiotou, P. (2021). A critical review of selected literature on learner-centered interactions in online learning. Electronic Journal of E-Learning, 19(5), 349–362. https://doi.org/10.34190/ejel.19.5.2469 19. Keyton, J. (2016). Communication research: Asking questions, finding answers (4th ed.). McGraw-Hill Education. 20. Khalil, M., & Ebner, M. (2017). Clustering patterns of engagement in massive open online courses (MOOCs): The use of learning analytics to reveal student categories. Journal of Computing in Higher Education, 29(1), 114–132. https://doi.org/10.1007/s12528-016-9130-3 21. Krejcie, V. R., & Morgan, W. D. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610. 22. Kuo, Y. C., Walker, A. E., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. International Review of Research in Open and Distance Learning, 14(1), 16–39. https://doi.org/10.19173/irrodl. v14i1.1338 23. Kuo, Y. C., Walker, A. E., Belland, B. R., Schroder, K. E. E., & Kuo, Y. T. (2014). A case study of integrating Interwise: Interaction, internet self-efficacy, and satisfaction in synchronous online learning environments. International Review of Research in Open and Distance Learning, 15(1), 161–181. https://doi.org/10.19173/irrodl.v15i1.1664 24. Li, K., & Moore, D. R. (2018). Motivating students in massive open online courses (MOOCs) using the attention, relevance, confidence, satisfaction (ARCS) model. Journal of Formative Design in Learning, 2(2), 102–113. https://doi.org/10.1007/s41686-018-0021-9 25. Liaw, S. S., & Huang, H. M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers and Education, 60(1), 14–24. https://doi.org/10.1016/j.c ompedu.2012.07.015 26. Lowenthal, P. R., & Dunlap, J. C. (2018). Investigating students’ perceptions of instructional strategies to establish social presence. Distance Education, 39(3), 281–298. https://doi.org/10.1080/01587919.2018.1476844 27. MacKenzie, L. M. (2019). Improving learning outcomes: Unlimited vs. limited attempts and time for supplemental interactive online learning activities. Journal of Curriculum and Teaching, 8(4), 36. https://doi.org/10.5430/jct.v8n4p36 28. Means, B., Toyama, Y., Murphy, R., & Baki, M. (2013). The effectiveness of online and blended learning: A meta-analysis of the empirical literature. Teachers College Record, 115(3), 1–47. http://0-www.tcrecord.org.oasis.unisa.ac.za/library/content. asp?contentid=16882 29. Mehall, S. (2020). Purposeful interpersonal interaction in online learning: What is it and how is it measured? Online Learning Journal, 24(1), 182–204. https://doi.org/10.24059/ olj.v24i1.2002 30. Moore, J. L., Dickson-Deane, C., & Galyen, K. (2011). E-learning, online learning, and distance learning environments: Are they the same? Internet and Higher Education, 14(2), 129–135. https://doi.org/10.1016/j.iheduc.2010.10.001 31. Moore, M. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1–7. 32. Oyarzun, B., Barreto, D., & Conklin, S. (2018). Instructor social presence effects on learner social presence, achievement, and satisfaction. TechTrends, 62(6), 625–634. https://doi.org/10.1007/s11528-018-0299-0 33. Oyarzun, B., Stefaniak, J., Bol, L., & Morrison, G. R. (2018). Effects of learner-to-learner interactions on social presence, achievement, and satisfaction. Journal of Computing in Higher Education, 30(1), 154–175. https://doi.org/10.1007/s12528-017-9157-x 34. Paechter, M., Maier, B., & Macher, D. (2010). Students’ expectations of, and experiences in e-learning: Their relation to learning achievements and course satisfaction. Computers and Education, 54(1), 222–229. https://doi.org/10.1016/j.compedu.2009.08.005 35. Panigrahi, R., Srivastava, P. R., & Sharma, D. (2018). Online learning: Adoption, continuance, and learning outcome—A review of literature. International Journal of Information Management, 43, 1–14. https://doi.org/10.1016/j.ijinfomgt.2018.05.005 36. Preacher, K. J., & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879–891. 37. Rabin, E., Kalman, Y. M., & Kalz, M. (2019). An empirical investigation of the antecedents of learner-centered outcome measures in MOOCs. International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0144-3 38. Ramayah, T., Cheah, J., Chuah, F., Ting, H., & Memon, M. A. (2018). Partial least squares structural equation modeling (PLS-SEM) using SmartPLS 3.0. In An updated guide and practical guide to statistical analysis. Pearson. 39. Razali, S. N., Ahmad, M. H., & Noor, H. A. M. (2020). Implications of learning interaction in online project-based collaborative learning. Journal of Computational and Theoretical Nanoscience, 17(2), 681–688. https://doi.org/10.1166/jctn.2020.8831 40. Shelton, B. E., Hung, J. L., & Lowenthal, P. R. (2017). Predicting student success by modeling student interaction in asynchronous online courses. Distance Education, 38(1), 59–69. https://doi.org/10.1080/01587919.2017.1299562 41. Starr Jr, R. G., Zhu, A. Q., Frethey-Bentham, C., & Brodie, R. J. (2020). Peer-to-peer interactions in the sharing economy: Exploring the role of reciprocity within a Chinese social network. Australasian Marketing Journal, 28(3), 67-80. https://doi.org/10.1016/ j.ausmj.2020.06.002 42. Waddington, J. (2023). Self-efficacy. ELT Journal, 77(2), 237-240. https://doi.org/10.1093/elt/ ccac046 43. Wei, H. C., & Chou, C. (2020). Online learning performance and satisfaction: Do perceptions and readiness matter? Distance Education, 41(1), 48–69. https://doi.org/10.1080/01587919.2020.1724768 44. Yokoyama, S. (2019). Academic self-efficacy and academic performance in online learning: A mini review. Frontiers in Psychology, 9, 2794. https://doi.org/10.3389/fpsyg.2018. 02794 45. Yu, Z. (2022). A meta-analysis and bibliographic review of the effect of nine factors on online learning outcomes across the world. Education and Information Technologies, 27(2), 2457–2482. https://doi.org/10.1007/s10639-021-10720-y 46. Yunusa, A. A., & Umar, I. N. (2021). A scoping review of critical predictive factors (CPFs) of satisfaction and perceived learning outcomes in E-learning environments. Education and Information Technologies, 26(1), 1223-1270. https://doi.org/10.1007/s10639-020-10286-1 47. Zheng, B., Lin, C. H., & Kwon, J. B. (2020). The impact of learner-, instructor-, and course-level factors on online learning. Computers & Education, 150, 103851. https://doi.org/10.1016/j.compedu.2020.103851 48. Zimmerman, E. (2009). Reconceptualizing the role of creativity in art education theory and practice. Studies in Art Education, 50(4), 382-399. https://doi.org/10.1080/00393541.2009.11518783