Teachers in the Virtual Classroom: Attitudes and Behavior toward Collaborative Online Learning

The increasing integration of digital technologies in education has reshaped instructional practices, with Collaborative Online Learning (COL) emerging as an important strategy to enhance engagement, interaction, and learning outcomes in higher education. This study examined the attitudes and behaviors of College of Teacher Education Arts and Sciences teachers toward COL and determined whether significant relationships exist between these variables and selected demographic profiles. A descriptive-comparative-correlational survey design was utilized, involving 22 faculty members who responded to a structured questionnaire assessing demographic characteristics, attitudes, and behavioral engagement toward COL.
Results revealed that respondents demonstrated a generally negative attitude toward COL, despite exhibiting a moderate level of behavioral engagement in related practices. This suggests that while teachers may still engage in COL activities due to institutional or instructional requirements, their perceptions remain less favorable. Further analysis showed no significant relationship between attitude and behavior (p > 0.05), indicating that behavioral engagement does not necessarily reflect positive attitudes. Likewise, demographic variables such as age, gender, and educational attainment did not significantly influence either attitude or behavior, suggesting a relatively uniform response pattern across groups.
The study concludes that there is a persistent gap between teachers’ perceptions and their actual engagement in COL practices. To address this, the proposed COLLAB-TEACH Program is recommended to strengthen teachers’ digital competence, confidence, and pedagogical readiness in implementing collaborative online learning strategies. The findings highlight the need for sustained institutional support, targeted professional development, and policy interventions to improve the effective and meaningful integration of COL in higher education.

Collaborative Online Learning, Digital Education, Instructional Technology

1. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211. [Google Scholar] [Crossref]

2. Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. [Google Scholar] [Crossref]

3. Ajzen, I. (2020). The theory of planned behavior: Frequently asked questions. Human Behavior and Emerging Technologies, 2(4), 314–324. https://doi.org/10.1002/hbe2.195 [Google Scholar] [Crossref]

4. Bokolo, A. J. (2021). Predicting academic staffs’ behavior intention and actual use of blended learning in higher education. Technology, Knowledge and Learning, 28, 1223–1269. [Google Scholar] [Crossref]

5. Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2021). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 18(1), 1–24. https://doi.org/10.1186/s41239-021-00282-0 [Google Scholar] [Crossref]

6. Bond, M., Bedenlier, S., Marín, V. I., & Händel, M. (2023). Emergency remote teaching in higher education: Mapping the first global online semester. International Journal of Educational Technology in Higher Education, 20(1), 1–24. https://doi.org/10.1186/s41239-021-00282-x [Google Scholar] [Crossref]

7. Cheng, S. L., & Xie, K. (2021). Why college students procrastinate in online courses: A self-regulated learning perspective. Internet and Higher Education, 50, 100807. https://doi.org/10.1016/j.iheduc.2021.100807 [Google Scholar] [Crossref]

8. Chou, C., & Chou, C. (2021). Investigating teachers’ continuance intention toward online teaching. Educational Technology Research and Development, 69, 1–20. [Google Scholar] [Crossref]

9. Desjardins, R., & Ioannidou, A. (2021). The political economy of adult learning systems. European Journal of Education, 56(2), 1–15. https://doi.org/10.1111/ejed.12449 [Google Scholar] [Crossref]

10. Desjardins, R., & Warnke, A. J. (2022). Ageing and skills: The role of experience in professional development. International Journal of Lifelong Education, 41(2), 1–18. https://doi.org/10.1080/02601370.2021.1960925 [Google Scholar] [Crossref]

11. Hamiza, H., et al. (2020). Adoption of e-learning platforms based on the theory of planned behavior. Computers & Education, 150, 103–112. [Google Scholar] [Crossref]

12. Hodges, C., Moore, S., Lockee, B., Trust, T., & Bond, A. (2020). 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 [Google Scholar] [Crossref]

13. Hung, M. L., & Jeng, I. (2020). Factors influencing educators’ intention to use online teaching platforms. Educational Technology & Society, 23(3), 45–58. [Google Scholar] [Crossref]

14. Khan, M. A., et al. (2022). Cultural influences on acceptance of distance education. International Journal of Educational Research, 114, 102–110. [Google Scholar] [Crossref]

15. König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19: School teachers’ attitudes and behaviors. European Journal of Teacher Education, 43(4), 608–622. https://doi.org/10.1080/02619768.2020.1809650 [Google Scholar] [Crossref]

16. König, J., Jäger-Biela, D. J., & Glutsch, N. (2020). Adapting to online teaching during COVID-19: Teachers’ competence and confidence. European Journal of Teacher Education, 43(4), 608–622. https://doi.org/10.1080/02619768.2020.1809650 [Google Scholar] [Crossref]

17. Lung-Guang, N. (2020). Self-regulated learning and planned behavior in MOOCs. Education and Information Technologies, 25, 1–15. [Google Scholar] [Crossref]

18. Maheshwari, G. (2021). Factors influencing students’ intention to study online. Education and Information Technologies, 26, 1–18. [Google Scholar] [Crossref]

19. Martin, F., Sun, T., & Westine, C. D. (2022). A systematic review of research on online teaching and learning from 2009 to 2021. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009 [Google Scholar] [Crossref]

20. Martin, F., Sun, T., & Westine, C. D. (2022). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009 [Google Scholar] [Crossref]

21. Martin, F., Sun, T., & Westine, C. D. (2022). A systematic review of research on online teaching and learning from 2009 to 2018. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009 [Google Scholar] [Crossref]

22. Martin, F., Sun, T., & Westine, C. D. (2022). Online teaching and learning: A systematic review. Computers & Education, 159, 104009. https://doi.org/10.1016/j.compedu.2020.104009 [Google Scholar] [Crossref]

23. Nie, L., et al. (2020). Mobile learning adoption using the theory of planned behavior. Computers in Human Behavior, 107, 106–308. [Google Scholar] [Crossref]

24. OECD. (2021). Teachers and school leaders as lifelong learners. https://www.oecd.org/education/teachers-and-school-leaders-as-lifelong-learners.htm [Google Scholar] [Crossref]

25. OECD. (2023). Education at a glance 2023: OECD indicators. https://www.oecd.org/education/education-at-a-glance/ [Google Scholar] [Crossref]

26. Organisation for Economic Co-operation and Development (OECD). (2024). Digital education outlook 2024: Towards effective collaborative learning. OECD Publishing. [Google Scholar] [Crossref]

27. Porter, S. R., & Whitcomb, M. E. (2021). Non-response in student surveys: The role of demographics. Research in Higher Education, 62(4), 1–15. https://doi.org/10.1007/s11162-020-09587-4 [Google Scholar] [Crossref]

28. Pratama, A. (2021). Mobile learning acceptance among teachers. Journal of Educational Computing Research, 59(5), 1–17. [Google Scholar] [Crossref]

29. Rapanta, C., Botturi, L., Goodyear, P., Guàrdia, L., & Koole, M. (2021). Online university teaching during and after the COVID-19 crisis. Postdigital Science and Education, 3, 715–736. https://doi.org/10.1007/s42438-020-00155-y [Google Scholar] [Crossref]

30. Scherer, R., Siddiq, F., & Tondeur, J. (2021). The technology acceptance model (TAM): A meta-analysis of teachers’ attitudes toward technology use. Educational Technology Research and Development, 69, 1–30. https://doi.org/10.1007/s11423-020-09850-2 [Google Scholar] [Crossref]

31. Songkram, N., & Chootongchai, S. (2022). Continuance intention in educational technology. Sustainability, 14(10), 1–15. [Google Scholar] [Crossref]

32. Teo, T., et al. (2021). Teachers’ technology acceptance and behavioral intention. Education and Information Technologies, 26, 1–22. [Google Scholar] [Crossref]

33. Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 189–199. https://www.learntechlib.org/primary/p/215995/ [Google Scholar] [Crossref]

34. Trust, T., & Whalen, J. (2020). Should teachers be trained in emergency remote teaching? Lessons learned from the COVID-19 pandemic. Journal of Technology and Teacher Education, 28(2), 189–199. [Google Scholar] [Crossref]

35. UNESCO. (2021). Reimagining our futures together: A new social contract for education. https://unesdoc.unesco.org/ark:/48223/pf0000379707 [Google Scholar] [Crossref]

36. UNESCO. (2022). Global education monitoring report 2022. https://www.unesco.org/en/education/gem-report [Google Scholar] [Crossref]

37. UNESCO. (2022). Global education monitoring report 2022: Gender report. https://www.unesco.org/en/education/gem-report [Google Scholar] [Crossref]

38. World Bank. (2022). Remote learning during COVID-19: Lessons from today, principles for tomorrow. https://www.worldbank.org [Google Scholar] [Crossref]

39. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2022). Systematic review of artificial intelligence applications in higher education. International Journal of Educational Technology in Higher Education, 19(1), 1–27. https://doi.org/10.1186/s41239-021-00292-y [Google Scholar] [Crossref]

• Assessment of the Role of Artificial Intelligence in Repositioning TVET for Economic Development in Nigeria
• Teachers’ Use of Assure Model Instructional Design on Learners’ Problem Solving Efficacy in Secondary Schools in Bungoma County, Kenya
• “E-Booksan Ang Kaalaman”: Development, Validation, and Utilization of Electronic Book in Academic Performance of Grade 9 Students in Social Studies
• Analyzing EFL University Students’ Academic Speaking Skills Through Self-Recorded Video Presentation
• Major Findings of The Study on Total Quality Management in Teachers’ Education Institutions (TEIs) In Assam – An Evaluative Study