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Driving forces of student satisfaction with online learning in the context of the COVID-19 pandemic: Evidence from Viet Nam

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Abstract

At a time when the world has suffered such an unprecedented event as the COVID-19 pandemic, it is essential to conduct research to evaluate the relationship of student satisfaction with interaction, Internet self-efficacy, and self-regulated learning in a fully online learning environment. The results of a survey of 290 students at a university in Viet Nam, using partial least squares structural equation modelling (PLS-SEM), indicate that four types of interaction (learner–learner interaction, learner–instructor interaction, learner–content interaction, and learner–technology interaction) positively and significantly affected students’ satisfaction with online learning, whereas Internet self-efficacy and self-regulated learning were not found to be significant predictors. These findings are crucial for enhancing the quality of online learning, which is regarded as not only the best cure for the massive global crisis COVID-19 has caused in education but also an innovative advancement compared with traditional face-to-face education. The authors discuss practical implications for instructional and course design, as well as directions for future research.

Résumé

Les moteurs de satisfaction des étudiants en matière d’apprentissage en ligne pendant la pandémie de COVID-19 à l’exemple du Viêt Nam – À une époque où le monde a été frappé par l’évènement sans précédent qu’a constitué la pandémie de COVID-19, il est essentiel de mener des recherches pour évaluer le rapport entre la satisfaction des étudiants, les interactions, l’auto-efficacité sur Internet et l’apprentissage autorégulé dans un environnement d’apprentissage intégralement en ligne. Les résultats d’une enquête menée auprès de 290 étudiants d’une université au Viêt Nam sur la base d’une modélisation par équations structurelles basée sur la méthode PLS (PLS-SEM) indiquent que quatre types d’interactions (interactions apprenants-apprenants, interactions apprenants-enseignants, interactions apprenants-contenus et interactions apprenants-technologie) influent positivement et considérablement sur la satisfaction des étudiants en matière d’apprentissage en ligne, tandis qu’ils n’ont pas permis de déduire que l’auto-efficacité sur Internet et l’apprentissage autorégulé étaient des prédicteurs significatifs. Ces résultats sont primordiaux pour améliorer la qualité de l’apprentissage en ligne considéré non seulement comme le meilleur remède à la crise mondiale massive provoquée par la COVID-19 dans l’éducation, mais aussi comme un progrès innovant par rapport à l’éducation traditionnelle en présentiel. Les auteurs examinent les implications pratiques pour la conception de l’enseignement et des cours, mais aussi pour l’orientation de la recherche à venir.

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Notes

  1. According to its own website, “The Online Learning Consortium™ (OLC) is a collaborative community of higher education leaders and innovators, dedicated to advancing quality digital teaching and learning experiences designed to reach and engage the modern learner – anyone, anywhere, anytime” (OLC n.d.).

References

  • Abdel-Jaber, H. (2017). Experimental analysis of students’ satisfaction factors in E-learning environment: A case study on Saudi Arabian University. Journal of Information & Knowledge Management, 16(2), Art. no. 1750018. https://doi.org/10.1142/s0219649217500186

  • Abrami, P. C., Bernard, R. M., Bures, E. M., Borokhovski, E., & Tamim, R. M. (2011). Interaction in distance education and online learning: Using evidence and theory to improve practice. Journal of Computing in Higher Education, 23(2–3), 82–103. https://doi.org/10.1007/s12528-011-9043-x

    Article  Google Scholar 

  • Agan, Y., Acar, M. F., & Borodin, A. (2013). Drivers of environmental processes and their impact on performance: A study of Turkish SMEs. Journal of Cleaner Production, 51, 23–33. https://doi.org/10.1016/j.jclepro.2012.12.043

    Article  Google Scholar 

  • Ali, A., & Ahmad, I. (2011). Key factors for determining student satisfaction in distance learning courses: A study of Allama Iqbal Open University. Contemporary Educational Technology, 2(2), 118–134. https://doi.org/10.30935/cedtech/6047

  • Allen, I. E., & Seaman, J. (2013). Changing course: Ten years of tracking online education in the United States. Sloan Consortium. Babson Park, MA: Babson Survey Research Group and Quahog Research Group. Retrieved 2 November 2023 from http://www.onlinelearningsurvey.com/reports/changingcourse.pdf

  • 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

  • Alqurashi, E. (2019). Predicting student satisfaction and perceived learning within online learning environments. Distance Education, 40(1), 133–148. https://doi.org/10.1080/01587919.2018.1553562

    Article  Google Scholar 

  • 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.

    Google Scholar 

  • Battalio, J. (2007). Interaction online: A reevaluation. Quarterly Review of Distance Education, 8(4), 339–352.

    Google Scholar 

  • BBC News (2022). Giáo dục VN: Giảng viên Czech nói sinh viên ở Sài Gòn ‘thụ động, ít nói, ít hỏi’ [Education in Vietnam: Czech lecturer says students in Saigon are ‘passive, talk less, ask less’]. BBC News Tiếng Việt, 17 January [online news item]. Retrieved 31 May 2023 from https://www.bbc.com/vietnamese/vietnam-59964528

  • Beldarrain, Y. (2008). Engaging the 21st century learner: An exploratory study of the relationship between interaction and achievement in the virtual high school. Doctoral thesis, Capella University, Minneapolis, MS. Retrieved 2 November 2023 from https://www.learntechlib.org/p/115817/

  • Bell, P. D. (2006). Can factors related to self-regulated learning and epistemological beliefs predict learning achievement in undergraduate asynchronous web-based courses? Perspectives in Health Information Management, 3, Art. no. 7.

  • Beqiri, M. S., Chase, N. M., & Bishka, A. (2009). Online course delivery: An empirical investigation of factors affecting student satisfaction. Journal of Education for Business, 85(2), 95–100. https://doi.org/10.1080/08832320903258527

    Article  Google Scholar 

  • Berge, Z. L. (1999). Interaction in post-secondary web-based learning. Educational technology, 39(1), 5–11.

    Google Scholar 

  • Bolliger, D. U. (2004). Key factors for determining student satisfaction in online courses. International Journal on E-learning, 3(1), 61–67.

    Google Scholar 

  • Bray, E., Aoki, K., & Dlugosh, L. (2008). Predictors of learning satisfaction in Japanese online distance learners. The International Review of Research in Open and Distance Learning, 9(3), Art. no. 525. https://doi.org/10.19173/irrodl.v9i3.525

  • Chekima, B., Wafa, S. A. W. S. K., Igau, O. A., Chekima, S., & Sondoh, S. L. (2016). Examining green consumerism motivational drivers: Does premium price and demographics matter to green purchasing? Journal of Cleaner Production, 112(Part 4), 3436–3450. https://doi.org/10.1016/j.jclepro.2015.09.102

  • Chen, Y.-J., & Chen, P.-C. (2007). Effects of online interaction on adult students’ satisfaction and learning. The Journal of Human Resource and Adult Learning, 3(2), 78–89.

    Google Scholar 

  • Cho, M.-H., & Cho, Y. (2017). Self-regulation in three types of online interaction: A scale development. Distance Education, 38(1), 70–83. https://doi.org/10.1080/01587919.2017.1299563

    Article  Google Scholar 

  • 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. https://doi.org/10.1016/j.iheduc.2012.11.001

    Article  Google Scholar 

  • Chong, S. M. (1998). Models of asynchronous computer conferencing for collaborative learning in large college classes. In C. J. Bonk & K. S. King (Eds.), Electronic collaborators: Learner-centered technologies for literacy, apprenticeship, and discourse (pp. 157–182). Mahwah, NJ: Lawrence Erlbaum Associates.

    Google Scholar 

  • Choy, S., McNickle, C., & Clayton, B. (2002). Learner expectations and experiences: An examination of student views of support in online learning. Leabrook, SA: National Centre for Vocational Education Research (NCVER). Retrieved 2 November 2023 from https://ncver.edu.au/__data/assets/file/0008/10133/learner-expectations-and-experiences-806.pdf

  • 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. Computers & Education, 55(1), 145–154. https://doi.org/10.1016/j.compedu.2009.12.011

    Article  Google Scholar 

  • Das, A., Lam, T. K., Thomas, S., Richardson, J., Kam, B. H., Lau, K. H., & Nkhoma, M. Z. (2019). Flipped classroom pedagogy: Using pre-class videos in an undergraduate business information systems management course. Education + Training, 61(6), 756–774. https://doi.org/10.1108/et-06-2018-0133

  • DeBourgh, G. A. (1999). Technology is the tool, teaching is the task: Student satisfaction in distance learning. In J. Price, J. Willis, D. Willis, M. Jost, & S. Boger-Mehall (Eds), Proceedings of SITE 1999 – Society for Information Technology & Teacher Education International Conference (pp. 131–137). Waynesville, NC: Association for the Advancement of Computing in Education (AACE). Retrieved 2 November 2023 from https://www.learntechlib.org/p/7521/

  • Dinh, L. P., & Nguyen, T. T. (2020). Pandemic, social distancing, and social work education: Students’ satisfaction with online education in Vietnam. Social Work Education, 39(8), 1074–1083. https://doi.org/10.1080/02615479.2020.1823365

    Article  Google Scholar 

  • Eastin, M. S., & LaRose, R. (2000). Internet self-efficacy and the psychology of the digital divide. Journal of Computer-Mediated Communication, 6(1), Art. no. JCMC611. https://doi.org/10.1111/j.1083-6101.2000.tb00110.x

  • Ekwunife-Orakwue, K. C. V., & Teng, T.-L. (2014). The impact of transactional distance dialogic interactions on student learning outcomes in online and blended environments. Computers & Education, 78, 414–427. https://doi.org/10.1016/j.compedu.2014.06.011

    Article  Google Scholar 

  • Eom, S. B., Wen, H. J., & Ashill, N. (2006). The determinants of students’ perceived learning outcomes and satisfaction in university online education: An empirical investigation. Decision Sciences Journal of Innovative Education, 4(2), 215–235. https://doi.org/10.1111/j.1540-4609.2006.00114.x

    Article  Google Scholar 

  • Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.1177/002224378101800104

    Article  Google Scholar 

  • Goh, C. F., Tan, O. K., Rasli, A., & Choi, S. L. (2019). Engagement in peer review, learner-content interaction and learning outcomes. The International Journal of Information and Learning Technology, 36(5), 423–433. https://doi.org/10.1108/ijilt-04-2018-0038

    Article  Google Scholar 

  • Gray, J. A., & DiLoreto, M. (2016). The effects of student engagement, student satisfaction, and perceived learning in online learning environments. NCPEA International Journal of Educational Leadership Preparation, 11(1), n1. https://eric.ed.gov/?id=EJ1103654

  • Gunawardena, C. N., Lowe, C. A., & Anderson, T. (1997). Analysis of a global online debate and the development of an interaction analysis model for examining social construction of knowledge in computer conferencing. Journal of Educational Computing Research, 17(4), 397–431. https://doi.org/10.2190/7mqv-x9uj-c7q3-nrag

    Article  Google Scholar 

  • Hair, J. F., Hult, T., Ringle, C. M., & Sarstedt, M. (2022). A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles, CA: SAGE.

  • Hair, J. F., Sarstedt, M., Hopkins, L., Kuppelwieser, G., & V. (2014). Partial least squares structural equation modeling (PLS-SEM). European Business Review, 26(2), 106–121. https://doi.org/10.1108/EBR-10-2013-0128

    Article  Google Scholar 

  • Hair, J. F., Sarstedt, M., Pieper, T. M., & Ringle, C. M. (2012). The use of partial least squares structural equation modeling in strategic management research: A review of past practices and recommendations for future applications. Long Range Planning, 45(5–6), 320–340. https://doi.org/10.1016/j.lrp.2012.09.008

    Article  Google Scholar 

  • Hanoi Times (2023). Zalo remains Vietnam’s most-used messaging app. Ministry of Information and Communications (MIC) Portal, 26 April [online news item]. Retrieved 31 May 2023 from https://english.mic.gov.vn/Pages/TinTuc/tinchitiet.aspx?tintucid=157664#:~:text=The%20usage%20of%20Zalo%20rose,to%2025%20years%20old)%20users

  • Hara, N. (2000). Student distress in a web-based distance education course. Information, Communication & Society, 3(4), 557–579. https://doi.org/10.1080/13691180010002297

    Article  Google Scholar 

  • Hargis, J. (2000). The self-regulated learner advantage: Learning science on the internet. The Electronic Journal for Research in Science & Mathematics Education, 4(4), 1–8.

    Google Scholar 

  • Hawkins, A., Barbour, M. K., & Graham, C. R. (2011). Strictly business: Teacher perceptions of interaction in virtual schooling. International Journal of E-Learning & Distance Education, 25(2), Art. no. 726.

  • Hirumi, A. (2011). The design and sequencing of online and blended learning interactions: A framework for grounded design. Canadian Learning Journal, 16(2), 21–25.

    Google Scholar 

  • Ho, N. T. T., Sivapalan, S., Pham, H. H., Nguyen, L. T. M., Pham, A. T. V., & Dinh, H. V. (2020). Students’ adoption of e-learning in emergency situation: The case of a Vietnamese university during COVID-19. Interactive Technology and Smart Education, 18(2), 246–269. https://doi.org/10.1108/itse-08-2020-0164

    Article  Google Scholar 

  • Hodges, C. B. (2005). Self-regulation in web-based courses: A review and the need for research. Quarterly Review of Distance Education, 6(4), 375–383.

    Google Scholar 

  • Jan, S. K. (2015). The relationships between academic self-efficacy, computer self-efficacy, prior experience, and satisfaction with online learning. American Journal of Distance Education, 29(1), 30–40. https://doi.org/10.1080/08923647.2015.994366

    Article  Google Scholar 

  • Juwah, C. (2006). Interactions in online education: Implications for theory and practice. Routledge. https://doi.org/10.4324/9780203003435

    Book  Google Scholar 

  • Kang, M., & Duong, A. (2021). Student perceptions of first-time online learning during the COVID-19 pandemic in Vietnam. IE: Inquiry in Education, 13(1), Art. no. 8.

  • 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), 1–10. https://doi.org/10.1007/s40299-020-00509-x

    Article  Google Scholar 

  • Kauffman, H. (2015). A review of predictive factors of student success in and satisfaction with online learning. Research in Learning Technology, 23, Art. no. 26507. https://doi.org/10.3402/rlt.v23.26507

  • Kiriakidis, P. (2005). A path analysis of factors that effect student satisfaction. Doctoral thesis, Touro University International, Cypress, CA. Retrieved 2 November 2023 from https://www.proquest.com/docview/305341148/abstract

  • Kuo, Y.-C., Walker, A., & Schroder, K. (2010). Interaction and other variables as predictors of student satisfaction in online learning environments. In D. Gibson & B. Dodge (Eds), Proceedings of SITE 2010 – Society for Information Technology & Teacher Education International Conference (pp. 593–600). San Diego, CA: Association for the Advancement of Computing in Education (AACE). Retrieved 2 November 2023 from https://www.learntechlib.org/p/33407/

  • Kuo, Y.-C., Walker, A. E., 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 Distributed Learning, 14(1), 16–39. https://doi.org/10.19173/irrodl.v14i1.1338

  • 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. https://doi.org/10.1016/j.iheduc.2013.10.001

    Article  Google Scholar 

  • Kurucay, M., & Inan, F. A. (2017). Examining the effects of learner-learner interactions on satisfaction and learning in an online undergraduate course. Computers & Education, 115, 20–37. https://doi.org/10.1016/j.compedu.2017.06.010

    Article  Google Scholar 

  • Le, H. T., & Truong, C. T. (2021). Tertiary students’ perspectives on online learning during emergency remote teaching in the context of Covid-19: A case study. In P. V. P. Ho et al. (Eds), Proceedings of the 17th International Conference of the Asia Association of Computer-Assisted Language Learning (AsiaCALL 2021) (pp. 203–210). Dordrecht: Atlantis Press. https://doi.org/10.2991/assehr.k.210226.025

  • Lee, H.-J., & Rha, I. (2009). Influence of structure and interaction on student achievement and satisfaction in web-based distance learning. Journal of Educational Technology & Society, 12(4), 372–382.

    Google Scholar 

  • Liang, J.-C., & Tsai, C.-C. (2008). Internet self-efficacy and preferences toward constructivist Internet-based learning environments: A study of pre-school teachers in Taiwan. Journal of Educational Technology & Society, 11(1), 226–237.

    Google Scholar 

  • Lin, C.-H., Zheng, B., & Zhang, Y. (2017). Interactions and learning outcomes in online language courses. British Journal of Educational Technology, 48(3), 730–748. https://doi.org/10.1111/bjet.12457

    Article  Google Scholar 

  • Linh, T. (2020). Minister of education and training Phung Xuan Nha requests UNICEF’s support in distance learning. United Nations Children’s Fund Viet Nam (UNICEF Viet Nam) website, 21 April [online news item]. Retrieved 31 May 2023 from https://www.unicef.org/vietnam/stories/minister-education-and-training-phung-xuan-nha-requests-unicefs-support-distance-learning

  • Livingstone, S., & Helsper, E. (2010). Balancing opportunities and risks in teenagers’ use of the internet: The role of online skills and internet self-efficacy. New Media & Society, 12(2), 309–329. https://doi.org/10.1177/1461444809342697

    Article  Google Scholar 

  • Macon, D. K. (2011). Student satisfaction with online courses versus traditional courses: A meta-analysis. Doctoral thesis, Northcentral University, Scottsdale, AZ. Retrieved 2 November 2023 from https://www.proquest.com/docview/858611481/abstract

  • Maheshwari, G. (2021). Factors affecting students’ intentions to undertake online learning: An empirical study in Vietnam. Education and Information Technologies, 26(6), 6629–6649. https://doi.org/10.1007/s10639-021-10465-8

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Martin, F., Parker, M. A., & Ndoye, A. (2011). Measuring success in a synchronous virtual classroom. In S. B. Eom & J. B. Arbaugh (Eds), Student satisfaction and learning outcomes in E-learning: An introduction to empirical research (pp. 249–266). Hershey, PA: IGI Global.

  • 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(2), 219–251.

    Google Scholar 

  • Moore, J. (2014). Effects of online interaction and instructor presence on students’ satisfaction and success with online undergraduate public relations courses. Journalism & Mass Communication Educator, 69(3), 271–288. https://doi.org/10.1177/1077695814536398

    Article  Google Scholar 

  • Moore, J. C. (2005). The Sloan Consortium quality framework and the five pillars. Newburyport, MA: The Sloan Consortium. Retrieved 2 November 2023 from https://docplayer.net/13157453-The-sloan-consortium-quality-framework-and-the-five-pillars-by-janet-c-moore.html

  • Moore, M. G. (1989). Editorial: Three types of interaction. American Journal of Distance Education, 3(2), 1–7. https://doi.org/10.1080/08923648909526659

    Article  Google Scholar 

  • Moore, M. G., & Anderson, W. G. (2003). Handbook of distance education (2nd ed.). Mahwah, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Moore, M. G., & Kearsley, G. G. (1996). Distance education: A system view. Belmont, CA: Wadsworth.

    Google Scholar 

  • Nandi, D., Hamilton, M., & Harland, J. (2015). What factors impact student – content interaction in fully online courses. International Journal of Modern Education and Computer Science, 7(7), 28–35. https://doi.org/10.5815/ijmecs.2015.07.04

    Article  Google Scholar 

  • NCES (National Center for Education Statistics) (2019). Digest of education statistics 2019, Table 311.15. Washington, DC: U.S. Department of Education, National Center for Education Statistics. Retrieved 5 November 2023 from https://nces.ed.gov/programs/digest/d20/tables/dt20_311.15.asp

  • Nguyen, N. D. (2009). Kế toán xác định kết quả kinh doanh từ lý luận đến thực tiễn ứng dụng vào các doanh nghiệp Việt Nam [Accounting determines business results from theory to practice applied to Vietnamese enterprises]. University of Economics Ho Chi Minh City.

    Google Scholar 

  • Nguyen, V. A. (2017). Towards the implementation of an assessment-centred blended learning framework at the course level: A case study in a Vietnamese National University. The International Journal of Information and Learning Technology, 34(1), 20–30. https://doi.org/10.1108/ijilt-08-2016-0031

    Article  Google Scholar 

  • Nkhoma, C. A., Thomas, S., Nkhoma, M. Z., Sriratanaviriyakul, N., Truong, T. H., & Vo, H. X. (2018). Measuring the impact of out-of-class communication through instant messaging. Education + Training, 60(4), 318–334. https://doi.org/10.1108/et-12-2017-0196

  • Noel-Levitz, Inc. (2011). National Online Learners Priorities Report. Coralville, IA: Noel-Levitz, Inc. Retrieved 31 May 2023, from https://files.eric.ed.gov/fulltext/ED537550.pdf

  • OLC (Online Learning Consortium) (n.d.). About OLC [website]. Boston, MA: OLC. Retrieved 29 November 2023 from https://onlinelearningconsortium.org/about/

  • Oliver, K., Osborne, J., & Brady, K. (2009). What are secondary students’ expectations for teachers in virtual school environments? Distance Education, 30(1), 23–45. https://doi.org/10.1080/01587910902845923

    Article  Google Scholar 

  • Peterson, S. (2011). Self-regulation and online course satisfaction in high school. Doctoral thesis, University of Southern California, Los Angeles, CA. Dissertation Abstracts International, 71(10A), UMI No. 3466080. Retrieved 5 November 2023 from https://www.proquest.com/docview/884340797

  • Preacher, K. J., & Hayes, A. F. (2004). SPSS and SAS procedures for estimating indirect effects in simple mediation models. Behavior Research Methods, Instruments, & Computers, 36(4), 717–731. https://doi.org/10.3758/bf03206553

    Article  Google Scholar 

  • Purarjomandlangrudi, A., & Chen, D. (2020). Exploring the influence of learners’ personal traits and perceived course characteristics on online interaction and engagement. Educational Technology Research and Development, 68(5), 2635–2657. https://doi.org/10.1007/s11423-020-09792-3

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Robson, R. (2000). Report on learning technology standards. In J. Bourdeau & R. Heller (Eds), Proceedings of ED-MEDIA 2000--World Conference on Educational Multimedia, Hypermedia & Telecommunications (pp. 971–976). Montreal, QC: Association for the Advancement of Computing in Education (AACE). Retrieved 2 November 2023 from https://www.learntechlib.org/p/16192/

  • Rodriguez Robles, F. M. (2006). Learner characteristic, interaction and support service variables as predictors of satisfaction in web-based distance education. Doctoral thesis, The University of New Mexico, Albuquerque, NM. Dissertation Abstracts International, 67(7), UMI No.3224964. Retrieved 5 November 2023 from https://www.learntechlib.org/p/121606/

  • Sher, A. (2009). Assessing the relationship of student–instructor and student–student interaction to student learning and satisfaction in web-based online learning environment. Journal of Interactive Online Learning, 8(2), 102–120.

    Google Scholar 

  • Shi, J., Chen, Z., & Tian, M. (2011). Internet self-efficacy, the need for cognition, and sensation seeking as predictors of problematic use of the internet. Cyberpsychology, Behavior, and Social Networking, 14(4), 231–234. https://doi.org/10.1089/cyber.2009.0462

    Article  Google Scholar 

  • Shih, C.-C., & Gamon, J. (2001). Web-based learning: Relationships among student motivation, attitude, learning styles, and achievement. Journal of Agricultural Education, 42(4), 12–20. https://doi.org/10.5032/jae.2001.04012

    Article  Google Scholar 

  • Strachota, E. (2006). The use of survey research to measure student satisfaction in online courses. Paper presented at the Midwest Research-to-Practice Conference in Adult, Continuing, and Community Education, University of Missouri-St. Louis, MO, 4–6 October 2006. Retrieved 5 November 2023 from https://docplayer.net/9581930-The-use-of-survey-research-to-measure-student-satisfaction-in-online-courses-elaine-strachota-abstract.html

  • Thurmond, V., & Wambach, K. (2004). Understanding interactions in distance education: A review of the literature. International Journal of Instructional Technology & Distance Learning, 1(1), 9–26.

    Google Scholar 

  • To, L. T. T., & Hoang, M. K. (2021). Amidst the COVID-19 pandemic: Student challenges and lessons learned in Vietnam. AsTEN Journal of Teacher Education, special issue, 1, 31–35.

    Google Scholar 

  • Tsai, C.-C., Chuang, S.-C., Liang, J.-C., & Tsai, M.-J. (2011). Self-efficacy in Internet-based learning environments: A literature review. Journal of Educational Technology & Society, 14(4), 222–240.

    Google Scholar 

  • Viet Nam News (2020). Digital transformation in Viet Nam: From aspiration to reality. Viet Nam News, 17 December [online news item]. Retrieved 31 May 2023 from https://vietnamnews.vn/economy/829504/digital-transformation-in-viet-nam-from-aspiration-to-reality.html

  • 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

    Article  Google Scholar 

  • Wei, H.-C., Peng, H., & Chou, C. (2015). Can more interactivity improve learning achievement in an online course? Effects of college students’ perception and actual use of a course-management system on their learning achievement. Computers & Education, 83, 10–21. https://doi.org/10.1016/j.compedu.2014.12.013

    Article  Google Scholar 

  • Womble, J. C. (2007). E-learning: The relationship among learner satisfaction, self-efficacy, and usefulness. Doctoral thesis, Alliant International University, San Diego, CA. Retrieved 7 November 2023 from https://www.learntechlib.org/p/119496/

  • Wu, J.-H., Tennyson, R. D., & Hsia, T.-L. (2010). A study of student satisfaction in a blended e-learning system environment. Computers & Education, 55(1), 155–164. https://doi.org/10.1016/j.compedu.2009.12.012

    Article  Google Scholar 

  • Yukselturk, E., & Bulut, S. (2005). Relationships among self-regulated learning components, motivational beliefs and computer programming achievement in an online learning environment. Mediterranean Journal of Educational Studies, 10(1), 91–112.

    Google Scholar 

  • Yukselturk, E., & Bulut, S. (2007). Predictors for student success in an online course. Journal of Educational Technology & Society, 10(2), 71–83.

    Google Scholar 

  • Yukselturk, E., & Yildirim, Z. (2008). Investigation of interaction, online support, course structure and flexibility as the contributing factors to students’ satisfaction in an online certificate program. Journal of Educational Technology & Society, 11(4), 51–65.

    Google Scholar 

  • Zhang, Y., & Lin, C.-H. (2020). Student interaction and the role of the teacher in a state virtual high school: What predicts online learning satisfaction? Technology, Pedagogy and Education, 29(1), 57–71. https://doi.org/10.1080/1475939x.2019.1694061

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Zimmerman, B. J. (2000). Attaining self-regulation: A social cognitive perspective. In M. Boekaerts, P. R. Pintrich, & M. Zeidner (Eds.), Handbook of self-regulation (pp. 13–39). Ssan Diego, CA: Academic Press.

    Chapter  Google Scholar 

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Funding

This study was funded by the University of Economics Ho Chi Minh City.

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Authors and Affiliations

  1. Faculty of International Economics, Ho Chi Minh University of Banking, Ho Chi Minh City, Viet Nam

    Nhung Trinh

  2. School of Business Administration, University of Economics Ho Chi Minh City, Ho Chi Minh City, Viet Nam

    Thi Ngo & Cham Nguyen

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  1. Nhung Trinh
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  2. Thi Ngo
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  3. Cham Nguyen
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Correspondence to Nhung Trinh.

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Trinh, N., Ngo, T. & Nguyen, C. Driving forces of student satisfaction with online learning in the context of the COVID-19 pandemic: Evidence from Viet Nam. Int Rev Educ 69, 851–873 (2023). https://doi.org/10.1007/s11159-023-10033-x

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