Abstracts
Abstract
This study investigated the effects of interactional, motivational, self-regulatory, and situational factors on university students’ online learning outcomes and continuation intentions during the COVID-19 pandemic. Data were collected from 255 students taking a business course at a university in southern China. Hierarchical multiple regression analyses revealed that while family financial hardship caused by COVID-19 was a marginally significant negative predictor of students’ learning outcomes, learner–content interaction; instructors’ provision of e-resources, course planning, and organisation; and students’ intrinsic goal orientation and meta-cognitive self-regulation were significant positive predictors with the latter two sets of predictors mediating the effects of learner–instructor and learner–learner interactions, respectively. Multinominal logistic regression analyses showed that learner–instructor interaction, learner–content interaction, and private learning space were significant positive predictors of students’ intentions to continue with online learning, but learner–learner interaction was a significant negative predictor. These findings point to the differential effects of various types of interactional and situational factors on learning outcomes and continuation intentions, and the instructor- and learner-level factors that mediate the effects of learner–instructor and learner–learner interactions on learning outcomes. They contribute to our understandings of emergency online learning and provide implications for facilitating it.
Keywords:
- emergency online learning,
- motivation,
- self-regulation,
- learning outcomes,
- continuation intention
Download the article in PDF to read it.
Download
Appendices
Bibliography
- Abdullatif, H., & Velázquez-Iturbide, J. Á. (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
- Aguilera-Hermida, A. P. (2020). College students’ use and acceptance of emergency online learning due to COVID-19. International Journal of Educational Research Open, 1, Article 100011. https://doi.org/10.1016/j.ijedro.2020.100011
- Anderson, T. (2003). Getting the mix right again: An updated and theoretical rationale for interaction. The International Review of Research in Open and Distance Learning, 4(2). https://doi.org/10.19173/irrodl.v4i2.149
- Arbaugh, J. B. (2000). How classroom environment and student engagement affect learning in Internet-based MBA courses. Business Communication Quarterly, 63(4), 9-26. https://doi.org/10.1177/108056990006300402
- Bernard, R. M., Abrami, P. C., Borokhovski, E., Wade, A., Tamim, R., Surkes, M., & Bethel, E. C. (2009). A meta-analysis of three interaction treatments in distance education. Review of Educational Research, 79(3), 1243-1289. https://doi.org/10.3102/0034654309333844
- Broadbent, J., & Poon, W. (2015). Self-regulated learning strategies & academic achievement in online higher education learning environments: A systematic review. The Internet and Higher Education, 27, 1-13. https://doi.org/10.1016/j.iheduc.2015.04.007
- Cho, M., & Shen, D. (2013). Self-regulation in online learning. Distance Education, 34(3), 290-301. https://doi.org/10.1080/01587919.2013.835770
- Chu, R. J.-c. (2010). How family support and Internet self-efficacy influence the effects of e-learning among higher aged adults—Analyses of gender and age differences. Computers & Education, 55(1), 255-264. https://doi.org/10.1016/j.compedu.2010.01.011
- 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. https://doi.org/10.1111/dsji.12097
- Garrison, D. R., & Cleveland-Innes, M. (2005). Facilitating cognitive presence in online learning: Interaction is not enough. The American Journal of Distance Education, 19, 133-148. https://doi.org/10.1207/s15389286ajde1903_2
- Greenhow, C., & Lewin, C. (2021). Online and blended learning: Contexts and conditions for education in an emergency. British Journal of Educational Technology, 52(4), 1301-1305. https://doi.org/10.1111/bjet.13130
- Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020, March 27). The difference between emergency remote teaching and online learning. EDUCAUSE Review. https://er.educause.edu/articles/2020/3/the-difference-between-emergency-remote-teaching-and-online-learning
- Huang, L.-Q., Zhang, J., & Liu, Y. (2017). Antecedents of student MOOC revisit intention: Moderation effect of course difficulty. International Journal of Information Management, 37(2), 84-91. https://doi.org/10.1016/j.ijinfomgt.2016.12.002
- Ifinedo, P. (2017). Examining students’ intention to continue using blogs for learning: Perspectives from technology acceptance, motivational, and social-cognitive frameworks. Computers in Human Behavior, 72, 189-199. https://doi.org/10.1016/j.chb.2016.12.049
- Johnston, S. C., Greer, D., & Smith, S. J. (2014). Peer learning in virtual schools. International Journal of E-Learning & Distance Education, 28(1), 1-31. http://www.ijede.ca/index.php/jde/article/view/853
- Juwah, C. (Ed.). (2006). Interactions in online learning: Implications for theory and practice. Routledge. https://doi.org/10.4324/9780203003435
- Kuo, Y. C., Walker, A., Belland, B. R., & Schroder, K. E. E. (2013). A predictive study of student satisfaction in online education programs. The International Review of Research in Open and Distance Learning, 14(1), 16-39. https://doi.org/10.19173/irrodl.v14i1.1338
- 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. The International Review of Research in Open and Distributed Learning, 15(1). https://doi.org/10.19173/irrodl.v15i1.1664
- 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. Internet & Higher Education, 20, 35-50. https://doi.org/10.1016/j.iheduc.2013.10.001
- Li, Y., Nishimura, N., Yagami, H., Park, H.-S. (2021). An empirical study on online learners’ continuance intentions in China. Sustainability, 13(2), Article 889. https://doi.org/10.3390/su13020889
- Lin, C., Zheng, B., & Zhang, Y. (2017a). Interactions and learning outcomes in online language courses. British Journal of Educational Technology, 48(3), 730-748. https://doi.org/10.1111/bjet.12457
- Lin, C., Zhang, Y., & Zheng, B. (2017b). The roles of learning strategies and motivation in online language learning: A structural equation modeling analysis. Computers & Education, 113, 75-85. https://doi.org/10.1016/j.compedu.2017.05.014
- Liu, O. L. (2012). Student evaluation of instruction: In the new paradigm of distance education. Research in Higher Education, 53(4), 471-486. https://doi.org/10.1007/s11162-011-9236-1
- Lou, Y., Bernard, R. M., & Abrami, P. C. (2006). Media and pedagogy in undergraduate distance education: A theory-based meta-analysis of empirical literature. Educational Technology Research and Development, 54(2), 141-176. https://doi.org/10.1007/s11423-006-8252-x
- Luo, N., Zhang, M., & Qi, D. (2017). Effects of different interactions on students’ sense of community in e-learning environment. Computers & Education, 115, 153-160. https://doi.org/10.1016/j.compedu.2017.08.006
- Marks, R. B., Sibley, S. D., & Arbaugh, J. B. (2005). A structural equation model of predictors for effective online learning. Journal of Management Education, 29(4), 531-563. https://doi.org/10.1177/1052562904271199
- Miyazoe, T., & Anderson, T. (2010). The interaction equivalency theorem. Journal of Interactive Online Learning, 9(2), 94-104. http://www.ncolr.org/jiol/issues/pdf/9.2.1.pdf
- Moore, M. (1989). Three types of interaction. American Journal of Distance Education, 3(2), 1-6. https://doi.org/10.1080/08923648909526659
- Moore, M. (1997). Theory of transactional distance. In D. Keegan (Ed.), Theoretical principles of distance education (pp. 22-38). Routledge.
- Park, S. Y., Nam, M., & Cha, S. (2012). University students’ behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43, 592-605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
- Pintrich, P. R., & De Groot, E. V. (1990). Motivational and self-regulated learning components of classroom academic performance. Journal of Educational Psychology, 82(1), 33-40. https://doi.org/10.1037/0022-0663.82.1.33
- Pintrich, P. R., Smith, D. A. F., Garcia, T., & McKeachie, W. J. (1991). A manual for the use of the motivated strategies for learning questionnaire (MSLQ) (Technical Report No. 91-8-004). University of Michigan. https://files.eric.ed.gov/fulltext/ED338122.pdf
- 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
- Puzziferro, M. (2008). Online technologies self-efficacy and self-regulated learning as predictors of final grade and satisfaction in college-level online courses. American Journal of Distance Education, 22(2), 72-89. https://doi.org/10.1080/08923640802039024
- Rehm, M., Moukarzel, S., Daly, A. J., & del Fresno, M. (2021). Exploring online social networks of school leaders in times of COVID-19. British Journal of Educational Technology, 52, 1414-1433. https://doi.org/10.1111/bjet.13099
- Tallent-Runnels, M. K., Thomas, J. A., Lan, W. Y., Cooper, S., Ahern, T. C., Shaw, S. M., & Liu, X. (2006). Teaching courses online: A review of the research. Review of Educational Research, 76(1), 93-135. https://doi.org/10.3102/00346543076001093
- Tsai, Y. H., Lin, C. H., Hong, J. C., & Tai, K. H. (2018). The effects of metacognition on online learning interest and continuance to learn with MOOCs. Computers & Education, 121, 18-29. https://doi.org/10.1016/j.compedu.2018.02.011
- Wagner, E. D. (1994). In support of a functional definition of interaction. American Journal of Distance Education, 8(2), 6-29. https://doi.org/10.1080/08923649409526852
- Wang, J., Yang, Y., Li, H., & van Aalst, J. (2021). Continuing to teach in a time of crisis: The Chinese rural educational system’s response and student satisfaction and social and cognitive presence. British Journal of Educational Technology, 52(4), 1494-1512. https://doi.org/10.1111/bjet.13129
- Weiner, C. (2003). Key ingredients to online learning: Adolescent students study in cyberspace—The nature of the study. International Journal on E-Learning, 2(3), 44-50. https://www.learntechlib.org/primary/p/14497/
- Zhou, M. M. (2016). Chinese university students’ acceptance of MOOCs: A self-determination perspective. Computer & Education, 92-93, 194-203. https://doi.org/10.1016/j.compedu.2015.10.012
- Zhu, Y., Zhang, J. H., Au, W., & Yates, G. (2020). University students’ online learning attitudes and continuous intention to undertake online courses: A self-regulated learning perspective. Educational Technology Research and Development, 68, 1-35. https://doi.org/10.1007/s11423-020-09753-w
- Zimmerman, B. J. (1989). A social cognitive view of self-regulated academic learning. Journal of Educational Psychology, 81(3), 329-339. https://doi.org/10.1037/0022-0663.81.3.329
- Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In P. R. Pintrich & M. Boekaerts (Eds.), Handbook of self-regulation (pp. 13-39). Academic Press. https://doi.org/10.1016/B978-012109890-2/50031-7