Vol. 1 No. 3 (2017): Advances in Research on Social Networking in Open and Distributed Learning

A path analysis of educator perceptions of open educational resources using the technology acceptance model

Hope Kelley

Published 2018-09-12


  • open educational resources,
  • technology acceptance model

How to Cite

Kelley, H. “A Path Analysis of Educator Perceptions of Open Educational Resources Using the Technology Acceptance Model”. Classic: An OJS Theme Demo, vol. 1, no. 3, Sept. 2018, https://demo.publicknowledgeproject.org/ojs3/demo/index.php/classic/article/view/823.


Open educational resources (OER) are making their way into a variety of educational contexts from formal lesson planning to just in time learning. Educators and training professionals have been recognized as an important audience for these materials. The concepts of self-efficacy and outcome judgment from social cognitive learning theory serve as theoretical constructs to measure educator perceptions of OER. This study uses a path analysis, based on the technology acceptance model, to understand adoption of these resources by this audience with a particular emphasis on self-efficacy. Among the participants, three main groups were identified: K-12 educators, higher education professionals, and those involved in workplace training. A discriminant function analysis found that K-12 educators stood out as finding OER relevant to improving their practice. Recommendations are made in regards to an emphasis on easy to use designs to improve application self-efficacy of OER and instructional messaging for future K-12 educators.


  1. Adams, D., Nelson, R., & Todd, P. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly, 16, 227–250.
  2. Ally, M., Cleveland-Innes, M., & Boskic, N. (2006). Learners’ use of learning objects. Journal of Distance Education, 21(2), 44-57.
  3. Atkins, D. E., Brown, J. S., & Hammond, A. L. (2007). A review of the open educational resources (OER) movement: Achievements, challenges, and new opportunities. Creative common.
  4. Bandura, A. (1977). Social learning theory. Englewood Cliffs, NJ: Prentice-Hall, Inc.
  5. Bandura, A. (1982). Self-efficacy mechanism in human agency. American Psychologist, 37(2), 122-147.
  6. Bandura, A. (1989). Human agency in social cognitive theory. American Psychologist, 44, 1175-1184.
  7. Barabási, A. L. (2002). Linked: The new science of networks. Basic Books.
  8. Compeau, D. R., Higgins, C. A., & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145–158.
  9. Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues for field settings. Boston: Houghton Mifflin.
  10. Clements, K. I., & Pawlowski, J. M. (2012). User-oriented quality for OER: understanding teachers’ views on re-use, quality, and trust. Journal of Computer Assisted Learning, 28(1), 4-14.
  11. Creative Commons. (2013) OER policy registry. Retrieved from http://wiki.creativecommons.org/OER_Policy_Registry
  12. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 318-340.
  13. Davis, F. D., & Venkatesh, V. (1996). A critical assessment of potential measurement biases in the technology acceptance model. International Journal of Human-Computer Studies, 45, 19-45.
  14. de Kunder, M. (2012). The size of the world wide web. Retrieved from http://www.worldwidewebsize.com/
  15. Florida Distance Learning Consortium. (2013). Open access textbook and OER legislation and policy. Retrieved from http://www.openaccesstextbooks.org/legislation.html
  16. Hasson, D., & Arnetz, B. B. (2005). Validation and findings comparing VAS vs. Likert scales for psychosocial measurements. International Electronic Journal of Health Education, 8, 178-192.
  17. Hendrickson, A. Massey, P., & Cronan, T. (1993). On the test – retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17, 227-230.
  18. Hylén, J. (2006). Open educational resources: Opportunities and challenges. Proceedings of Open Education, 49-63.
  19. Lau, S., & Woods, P. (2009). Understanding learner acceptance of learning objects: The roles of learning object characteristics and individual differences. British Journal of Educational Technology, 40(6), 1059-1075.
  20. Liyanagunawardena, T. R., Adams, A. A., & Williams, S. A. (2013). MOOCs: A systematic study of the published literature 2008-2012. International Review of Research in Open & Distance Learning, 14(3).
  21. Mangan, K. (2012). MOOC mania. The Chronicle of Higher Education. Retrieved from http://chronicle.com/article/Massive-Excitement-About/134678/
  22. Moore, G. C., & Benbasat, I. (1996). Integrating diffusion of innovations and theory of reasoned action models to predict utilization of information technology by end-users. Diffusion and adoption of information technology (pp. 132-146).
  23. Murphy, C. A., Coover, D., & Owen, S. V. (1989). Development and validation of the computer self-efficacy scale. Educational and Psychological Measurement, 49(4), 893–899.
  24. Read, M. (2008). Cultural and educational drivers of educational content. The tower and the cloud. U.S.A.: Educause.
  25. Rogers, E. M. (1995). Diffusion of innovations. Simon and Schuster.
  26. Segars, A., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17, 517–525.
  27. Schmidt-Jones, C. (2012). An open educational resource supports a diversity of inquiry-based learning. International Review of Research in Open & Distance Learning, 13(1), 1-16.
  28. Schonfeld, R. C., & Houseright, R. (2010). Faculty survey 2009: Key strategic insights for libraries, publishers, and societies. Creative common.
  29. Stevens, J. P. (2012). Applied multivariate statistics for the social sciences. Routledge Academic.
  30. Suhr, D. (2008, November). Step your way through path analysis. In Western Users of SAS Software Conference Proceedings.
  31. United Nations Educational, Scientific and Cultural Organization. (2012). 2012 Paris OER Declaration. Retrieved from http://www.unesco.org/new/fileadmin/MULTIMEDIA/HQ/CI/CI/pdf/Events/Paris%20OER%20Declaration_01.pdf
  32. Wiley, D. (2008). Chapter 29: The learning objects literature. Handbook of research on educational communications and technology [electronic resource] (3rd ed.) New York: Lawrence Erlbaum Associates.
  33. Wright, S. (1921). Correlation and causation. Journal of Agricultural Research, 20(7), 557-585.
  34. Yi, M.Y., & Hwang, Y. (2002). Predicting the use of web-based information systems: Self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model. International Journal of Human-Computer Studies, 59, 431-449.