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Predicting educators’ use of Twitter for professional learning and development

  • Fei GaoEmail author
  • Lan Li
Article
  • 19 Downloads

Abstract

This paper proposes and tests a conceptual model of educators’ Twitter adoption for professional learning and development based on technology adoption model and related literature. To understand the factors that may affect educators’ adoption of Twitter for professional learning and development, a questionnaire was designed and administered to 305 participants with the majority (88.5%) being 31 or older, and a structural equation model was constructed to examine the relationships among the factors that affect this adoption process. The results suggest that the research framework integrating multiple perspectives provides a comprehensive understanding of educators’ intention and actual use of Twitter for professional learning and development. Specifically, educators’ attitude of adoption and the purpose of Twitter usage had a significant direct effect on educators’ intention and actual use of Twitter for professional learning and development. The study suggests, to encourage educators to adopt Twitter for professional learning and development, we need to help them understand the usefulness of Twitter, whether its usage fits with their learning tasks, as well as for what purposes they are going to use it.

Keywords

Professional development Social media Technology adoption Structural equation model Lifelong learning 

Notes

Compliance with ethical standards

Disclosure of potential conflicts of interest

The authors declare that they have no conflict of interest.

Research involving human participants and/or animals

This research involves human participants.

Informed consent

Informed consent was obtained from all who participated in the study. A consent form was signed electronically by the subjects before the data was collected to make the subjects eligible for participation. The data collected are kept confidential. The data do not identify the subjects from which it was collected, other than to the two investigators. Electronic data are stored in password protected files on a secure computer accessible only by the investigators.

References

  1. Arteaga Sánchez, R., Cortijo, V., & Javed, U. (2014). Students' perceptions of Facebook for academic purposes. Computers & Education, 70, 138–149.  https://doi.org/10.1016/j.compedu.2013.08.012.Google Scholar
  2. Bledsoe, T. S., Harmeyer, D., & Wu, S. F. (2014). Utilizing Twitter and #hashtags toward enhancing student learning in an online course environment. International Journal of Distance Education Technologies, 12(3), 75–83.Google Scholar
  3. Byrne, B. M. (2016). Structural equation modeling with AMOS: Basic concepts, applications, and programming (3rd ed.). New York: Routledge.Google Scholar
  4. Carpenter, J. P., & Krutka, D. G. (2014). How and why educators use Twitter: A survey of the field. Journal of Research on Technology in Education, 46(4), 414–434.  https://doi.org/10.1080/15391523.2014.925701.Google Scholar
  5. Carpenter, J. P., & Krutka, D. G. (2015). Engagement through microblogging: Educator professional development via Twitter. Professional Development in Education, 41(4), 707–728.  https://doi.org/10.1080/19415257.2014.939294.Google Scholar
  6. Carpenter, J. P., & Krutka, D. G. (2016). Participatory learning through social media: How and why social studies educators use Twitter. Contemporary Issues In Technology & Teacher Education, 16(1), 38–59.Google Scholar
  7. Chau, P. Y. K., & Hu, P. J.-H. (2001). Information technology acceptance by individual professionals: A model comparison approach. Decision Sciences, 32(4), 699–719.  https://doi.org/10.1111/j.1540-5915.2001.tb00978.x.Google Scholar
  8. Chen, C.-P., Lai, H.-M., & Ho, C.-Y. (2015). Why do teachers continue to use teaching blogs? The roles of perceived voluntariness and habit. Computers & Education, 82, 236–249.  https://doi.org/10.1016/j.compedu.2014.11.017.Google Scholar
  9. Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160–175.  https://doi.org/10.1016/j.compedu.2012.12.003.Google Scholar
  10. Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92(Supplement C), 37–52. doi: https://doi.org/10.1016/j.compedu.2015.09.013
  11. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340.  https://doi.org/10.2307/249008.Google Scholar
  12. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35, 982–1003.Google Scholar
  13. Dishaw, M. T., & Strong, D. M. (1999). Extending the technology acceptance model with task–technology fit constructs. Information & Management, 36(1), 9–21.  https://doi.org/10.1016/S0378-7206(98)00101-3.Google Scholar
  14. Dunlap, J. C., & Lowenthal, P. R. (2009). Horton hears a tweet. The EDUCAUSE Quarterly, 32(4).Google Scholar
  15. Ebner, M., & Maurer, H. (2009). Can weblogs and microblogs change traditional scientific writing? Future Internet, 1(1), 47–58.  https://doi.org/10.3390/fi1010047.Google Scholar
  16. Elkaseh, A. M., Wong, K. W., & Fung, C. C. (2016). Perceived ease of use and perceived usefulness of social media for e-learning in Libyan higher education: a structural equation modeling analysis. International Journal of Information and Education Technology, 6(3), 192–199.Google Scholar
  17. Fornell, C., & Larcker, D. F. (1981). Structural equation models with unobservable variables and measurement error: Algebra and statistics. Journal of Marketing Research, 18(3), 382–388.Google Scholar
  18. Gao, F. & Li, L. (2017). Examining a one-hour synchronous chat in a microblogging-based professional development community. British Journal of Educational Technology. 48(2), 332-347.  https://doi.org/10.1111/bjet.12384.
  19. Goodhue, D. L., & Thompson, R. L. (1995). Task-technology fit and individual performance. MIS Quarterly, 19(2), 213–236.Google Scholar
  20. Hair, J., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). New Jersey: Pearson Education.Google Scholar
  21. Han, I., & Shin, W. S. (2016). The use of a mobile learning management system and academic achievement of online students. Computers & Education, 102(Supplement C), 79–89.  https://doi.org/10.1016/j.compedu.2016.07.003.Google Scholar
  22. Hartwick, J., & Barki, H. (1994). Explaining the role of user participation in information system use. Management Science, 40(4), 440–465.  https://doi.org/10.1287/mnsc.40.4.440.Google Scholar
  23. Hennessy, C. M., Kirkpatrick, E., Smith, C. F., & Border, S. (2016). Social media and anatomy education: Using twitter to enhance the student learning experience in anatomy: Use of Twitter in anatomy education. Anatomical Sciences Education, 9(6), 505–515.  https://doi.org/10.1002/ase.1610.Google Scholar
  24. Hitchcock, L. I., & Young, J. A. (2016). Tweet, tweet!: Using live Twitter chats in social work education. Social Work Education, 35(4), 457–468.  https://doi.org/10.1080/02615479.2015.1136273.Google Scholar
  25. Holden, R. J., & Karsh, B.-T. (2010). The technology acceptance model: Its past and its future in health care. Journal of Biomedical Informatics, 43(1), 159–172.  https://doi.org/10.1016/j.jbi.2009.07.002.Google Scholar
  26. Kimmons, R., & Veletsianos, G. (2016). Education scholars’ evolving uses of Twitter as a conference backchannel and social commentary platform. British Journal of Educational Technology, 47(3), 445–464.Google Scholar
  27. Krutka, D. G., & Carpenter, J. P. (2016). Participatory learning through social media: How and why social studies educators use Twitter. Contemporary Issues in Technology and Teacher Education, 16(1), 38–59.Google Scholar
  28. Lai, C., Wang, Q., & Lei, J. (2012). What factors predict undergraduate students' use of technology for learning? A case from Hong Kong. Computers & Education, 59(2), 569–579.  https://doi.org/10.1016/j.compedu.2012.03.006.Google Scholar
  29. Lee, D. Y., & Lehto, M. R. (2013). User acceptance of YouTube for procedural learning: An extension of the technology acceptance model. Computers & Education, 61, 193–208.  https://doi.org/10.1016/j.compedu.2012.10.001.Google Scholar
  30. Lin, K.-M. (2011). E-learning continuance intention: Moderating effects of user e-learning experience. Computers & Education, 56(2), 515–526.  https://doi.org/10.1016/j.compedu.2010.09.017.Google Scholar
  31. Lu, H.-P., & Yang, Y.-W. (2014). Toward an understanding of the behavioral intention to use a social networking site: An extension of task-technology fit to social-technology fit. Computers in Human Behavior, 34(Supplement C), 323–332.  https://doi.org/10.1016/j.chb.2013.10.020.Google Scholar
  32. Marsh, H. W., & Hocevar, D. (1985). Application of confirmatory factor analysis to the study of self-concept: First- and higher order factor models and their invariance across groups. Psychological Bulletin, 97(3), 562–582.  https://doi.org/10.1037/0033-2909.97.3.562.Google Scholar
  33. Mathieson, K. (1991). Predicting user intentions: Comparing the technology acceptance model with the theory of planned behavior. Information Systems Research, 2(3), 173–191.  https://doi.org/10.1287/isre.2.3.173.Google Scholar
  34. Mazman, S. G., & Usluel, Y. K. (2010). Modeling educational usage of Facebook. Computers & Education, 55(2), 444–453.  https://doi.org/10.1016/j.compedu.2010.02.008.Google Scholar
  35. McGill, T. J., & Klobas, J. E. (2009). A task–technology fit view of learning management system impact. Computers & Education, 52(2), 496–508.  https://doi.org/10.1016/j.compedu.2008.10.002.Google Scholar
  36. Mulatiningsih, B., Partridge, H., & Davis, K. (2013). Exploring the role of twitter in the professional practice of LIS professionals: A pilot study. Australian Library Journal, 62(3), 204–217.  https://doi.org/10.1080/00049670.2013.806998.Google Scholar
  37. Noble, A., McQuillan, P., & Littenberg-Tobias, J. (2016). "A lifelong classroom": Social studies educators' engagement with professional learning networks on Twitter. Journal of Technology and Teacher Education, 24(2), 187–213.Google Scholar
  38. Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592–605.  https://doi.org/10.1111/j.1467-8535.2011.01229.x.Google Scholar
  39. Roberts, M. J., Perera, M., Lawrentschuk, N., Romanic, D., Papa, N., & Bolton, D. (2015). Globalization of continuing professional development by journal clubs via microblogging: A systematic review. Journal of Medical Internet Research, 17(4), e103–e103.  https://doi.org/10.2196/jmir.4194.Google Scholar
  40. Rodesiler, L. (2015). The nature of selected English teachers' online participation. Journal of Adolescent & Adult Literacy, 59(1), 31–40.  https://doi.org/10.1002/jaal.427.Google Scholar
  41. Rodesiler, L., & Pace, B. G. (2015). English teachers' online participation as professional development: A narrative study. English Education, 47(4), 347.Google Scholar
  42. Seyal, A. H., Rahman, M. N. A., & Rahim, M. M. (2002). Determinants of academic use of the internet: A structural equation model. Behaviour & Information Technology, 21(1), 71–86.  https://doi.org/10.1080/01449290210123354.Google Scholar
  43. Shang, R. A., Chen, Y. C., & Chen, C. M. (2007). Why people blog? An empirical investigations of the task technology fit model. PACIS 2007 Proceedings, 212–225.Google Scholar
  44. Shih, Y.-Y., & Chen, C.-Y. (2013). The study of behavioral intention for mobile commerce: Via integrated model of TAM and TTF. Quality & Quantity, 47(2), 1009–1020.  https://doi.org/10.1007/s11135-011-9579-x.Google Scholar
  45. Tabachnick, B. G., & Fidell, L. S. (2007). Using multivariate statistics (5th ed.). Boston: Allyn & Bacon/Pearson.Google Scholar
  46. Tarhini, A., Hone, K., & Liu, X. (2014). The effects of individual differences on e-learning users’ behaviour in developing countries: A structural equation model. Computers in Human Behavior, 41, 153–163.  https://doi.org/10.1016/j.chb.2014.09.020.Google Scholar
  47. Teo, T. (2011). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.  https://doi.org/10.1016/j.compedu.2011.06.008.Google Scholar
  48. Teo, T. (2016). Modelling Facebook usage among university students in Thailand: The role of emotional attachment in an extended technology acceptance model. Interactive Learning Environments, 24(4), 745–757.  https://doi.org/10.1080/10494820.2014.917110.Google Scholar
  49. Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.Google Scholar
  50. Wu, B., & Chen, X. H. (2017). Continuance intention to use MOOCs: Integrating the technology acceptance model (TAM) and task technology fit (TTF) model. Computers in Human Behavior, 67, 221–232.  https://doi.org/10.1016/j.chb.2016.10.028.Google Scholar
  51. Yen, D. C., Wu, C.-S., Cheng, F.-F., & Huang, Y.-W. (2010). Determinants of users’ intention to adopt wireless technology: An empirical study by integrating TTF with TAM. Computers in Human Behavior, 26(5), 906–915.  https://doi.org/10.1016/j.chb.2010.02.005.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Bowling Green State UniversityBowling GreenUSA
  2. 2.Bowling Green State UniversityBowling GreenUSA

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