Skip to main content

Advertisement

Log in

Teachers’ Readiness to Adopt Mobile Learning in Classrooms: A Study in Greece

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Mobile devices have become a learning tool with great potential in both formal and informal learning; however, mobile learning readiness research in school education is relatively limited. This study investigated teachers’ readiness to adopt mobile learning in K-12 classrooms. A questionnaire was administered to 920 teachers in Greece and four factors were extracted, Possibilities, Benefits, Preferences and External influences. Teachers, in general, expressed positive perceptions on mobile learning readiness. The highest percentage of agreement regarded the possibilities of mobile learning (over 60%). ICT training and attendance of ICT conferences, both affected positively teachers’ perceptions on mobile learning benefits and preferences. Teachers who use mobile devices in class reported significantly more positive perceptions on all factors, while gender or age had no impact on perceptions. There was a higher probability of mobile devices’ usage in class among teachers working in elementary schools (in comparison with those working in high schools or general/vocational lyceums). Stronger perceptions on mobile learning benefits, preferences and external influences were associated with an increased likelihood of using mobile devices in the classroom. Teachers’ readiness perceptions can be explored from a multi-dimensional perspective, and also be associated with mobile technology use in classrooms. Implications for teacher professional development, methodology and pedagogical practice are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Al-Furaih, S. A. A., & Al-Awidi, H. M. (2020). Teachers’ change readiness for the adoption of smartphone technology: Personal concerns and technological competency. Technology, Knowledge and Learning, 25, 409–432. https://doi.org/10.1007/s10758-018-9396-6.

    Article  Google Scholar 

  • Baek, Y., Zhang, H., & Yun, S. (2017). Teachers’ attitudes toward mobile learning in Korea. TOJET: The Turkish Online Journal of Educational Technology, 16(1), 154–163.

    Google Scholar 

  • Baydas, O., & Yilmaz, R. (2018). Pre-service teachers’ intention to adopt mobile learning: A motivational model. British Journal of Educational Technology, 49(1), 137–152.

    Article  Google Scholar 

  • Blackwell, C. K., Lauricella, A. R., & Wartella, E. (2014). Factors influencing digital technology use in early childhood education. Computers & Education, 77, 82–90.

    Article  Google Scholar 

  • Byrne, B. M. (2010). Structural equation modeling with AMOS: Basic concepts, applications, and programming (2nd ed.). NY: Routledge.

    Google Scholar 

  • Chang, C.-Y., & Hwang, G.-J. (2019). Trends in digital game-based learning in the mobile era: a systematic review of journal publications from 2007 to 2016. International Journal of Mobile Learning and Organisation, 13(1), 68–90.

    Article  Google Scholar 

  • Christensen, R., & Knezek, G. (2018). Reprint of readiness for integrating mobile learning in the classroom: Challenges, preferences and possibilities. Computers in Human Behavior, 78, 379–388.

    Article  Google Scholar 

  • Day, C. (2002). School reform and transitions in teacher professionalism and identity. International Journal of Educational Research, 37, 677–692.

    Article  Google Scholar 

  • Ditzler, C., Hong, E., & Strudler, N. (2016). How tablets are utilized in the classroom. Journal of Research on Technology in Education, 48(3), 181–193.

    Article  Google Scholar 

  • Domingo, M. G., & Garganté, A. B. (2016). Exploring the use of educational technology in primary education: Teachers’ perception of mobile technology learning impacts and applications’ use in the classroom. Computers in Human Behavior, 56, 21–28.

    Article  Google Scholar 

  • Ertmer, P. A. (1999). Addressing first- and second order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47–61.

    Article  Google Scholar 

  • Ertmer, P. A. (2005). Teacher pedagogical beliefs: The final frontier in our quest for technology integration?. Educational Technology Research and Development, 53(4), 25–39.

    Article  Google Scholar 

  • Ertmer, P. A., & Ottenbreit-Leftwich, A. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255–284.

    Article  Google Scholar 

  • Fokides, E., Atsikpasi, P., & Karageorgou, D. (2020). Tablets, plants, and primary school students: A study. Technology, Knowledge and Learning.. https://doi.org/10.1007/s10758-020-09445-7.

    Article  Google Scholar 

  • Fu, Q.-K., & Hwang, G.-J. (2018). Trends in mobile technology-supported collaborative learning: A systematic review of journal publications from 2007 to 2016. Computers & Education, 119, 129–143.

    Article  Google Scholar 

  • Grant, M. M. (2019). Difficulties in defining mobile learning: analysis, design characteristics, and implications. Education Technology Research & Development. https://doi.org/10.1007/s11423-018-09641-4.

    Article  Google Scholar 

  • Hayton, J. C., Allen, D. G., & Scarpello, V. (2004). Factor retention decisions in exploratory factor analysis: A tutorial on parallel analysis. Organizational Research Methods, 7(2), 191–205.

    Article  Google Scholar 

  • Hennessy, S., Wishart, J., Whitelock, D., Deaney, R., Brawn, R., la Velle, L., et al. (2007). Pedagogical approaches for technology-integrated science teaching. Computers & Education, 48(1), 137–152.

    Article  Google Scholar 

  • Henseler, J., Ringle, C. M., & Sarstedt, M. (2014). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135. https://doi.org/10.1007/s11747-014-0403-8.

    Article  Google Scholar 

  • Hosmer, D. W., Jr., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (3rd ed.). New York: Wiley.

    Book  Google Scholar 

  • Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indices in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1–55.

    Article  Google Scholar 

  • Ifenthaler, D., & Schweinbenz, V. (2013). The acceptance of Tablet-PCs in classroom instruction: The teachers’ perspective. Computers in Human Behavior, 29(3), 525–534.

    Article  Google Scholar 

  • Jorgensen, T. D., Pornprasertmanit, S., Schoemann, A. M., & Rosseel, Y. (2018). semTools: Useful tools for structural equation modeling. R package version 0.5-1. https://CRAN.R-project.org/package=semTools. Accessed 19 May 2019.

  • Khlaif, Z. (2017). Factors influencing teachers’ attitudes toward mobile technology integration in K-12. Technology, Knowledge and Learning, 23(1), 161–175. https://doi.org/10.1007/s10758-017-9311-6.

    Article  Google Scholar 

  • Khlaif, Z. (2018). Teachers’ perceptions of factors affecting their adoption and acceptance of mobile technology in K-12 settings. Computers in the Schools, 35(1), 49–67.

    Article  Google Scholar 

  • Kim, H. J., & Kim, H. (2017). Investigating Teachers’ Pedagogical Experiences with Tablet Integration in Korean Rural Schools. Asia-Pacific Education Researcher, 26(1–2), 107–116.

    Article  Google Scholar 

  • Kousloglou, M., & Syrpi, M. (2018). Perceptions of secondary school teachers on the use of handheld devices in schools as learning tools. 5th Pan-Hellenic Educational Conference of Central Macedonia “ICT use and integration in educational practice”, April 27-29, 2018, Thessaloniki (in Greek).

  • Kumar, B. A., & Chand, S. S. (2019). Mobile learning adoption: A systematic review. Education and Information Technologies, 24(1), 471–487.

    Article  Google Scholar 

  • Kwon, K., Ottenbreit-Leftwich, A. T., Sari, A., Khlaif, Z., Zhu, M., Nadir, H., et al. (2019). Teachers’ self-efficacy matters: Exploring the integration of mobile computing device in middle schools. Tech Trends. https://doi.org/10.1007/s11528-019-00402-5.

    Article  Google Scholar 

  • Leem, J., & Sung, E. (2019). Teachers’ beliefs and technology acceptance concerning smart mobile devices for SMART education in South Korea. British Journal of Educational Technology, 50(2), 601–613.

    Article  Google Scholar 

  • Lenhart, A., Ling, R., Campbell, S., & Purcell, K. (2010). Teens and mobile phones. Washington, DC: Pew Internet & American Life Project, 20. http://pewinternetorg/Reports/2012/Teens-and-smartphones.aspx. Accessed 15 Dec 2018.

  • Mardia, K. V. (1970). Measures of multivariate skewness and kurtosis with applications. Biometrika, 57(3), 519–530.

    Article  Google Scholar 

  • Montrieux, H., Courtois, C., Raes, A., Schellens, T., & De Marez, L. (2014). Mobile learning in secondary education: teachers’ and students’ perceptions and acceptance of tablet computers. International Journal of Mobile and Blended Learning, 6(2), 26–40.

    Article  Google Scholar 

  • Muthén, B. O. (1993). Goodness of fit with categorical and other non-normal variables. In K. A. Bollen & J. S. Long (Eds.), Testing Structural Equation Models (pp. 205–243). Newbury Park, CA: Sage.

    Google Scholar 

  • Nikolopoulou, K. (2018). Mobile learning usage and acceptance: perceptions of secondary school students. Journal of Computers in Education, 5(4), 499–519.

    Article  Google Scholar 

  • Nikolopoulou, K., & Gialamas, V. (2015). Barriers to the integration of computers in early childhood settings: Teachers’ perceptions. Education and Information Technologies, 20(2), 285–301.

    Article  Google Scholar 

  • Nikolopoulou, K., & Gialamas, V. (2017). High school pupils’ attitudes and self-efficacy of using mobile devices. Themes in Science & Technology Education, 10(2), 53–67.

    Google Scholar 

  • Nikolopoulou, K., & Kousloglou, M. (2019). Mobile learning in science: A study in secondary education in Greece. Creative Education, 10(6), 1271–1284.

    Article  Google Scholar 

  • O’Bannon, B., & Thomas, K. (2014). Teacher perceptions of using mobile phones in the classroom: Age matters! Computers & Education, 74, 15–25.

    Article  Google Scholar 

  • Ozdamli, F., & Uzunboylu, H. (2015). M-learning adequacy and perceptions of students and teachers in secondary schools. British Journal of Educational Technology, 46(1), 159–172.

    Article  Google Scholar 

  • Peng, H., Tsai, C.-C., & Wu, Y.-T. (2006). University students’ self-efficacy and their attitudes toward the internet: the role of students’ perceptions of the internet. Educational Studies, 32(1), 73–86.

    Article  Google Scholar 

  • Raykov, T. (2001). Estimation of congeneric scale reliability using covariance structure analysis with nonlinear constraints. The British Journal of Mathematical and Statistical Psychology, 54, 315–323. https://doi.org/10.1348/000711001159582.

    Article  Google Scholar 

  • Rosseel, Y. (2012). Lavaan: An R Package for Structural Equation Modeling. Journal of Statistical Software, 48(2), 1–36.

    Article  Google Scholar 

  • Somekh, B. (2008). Factors affecting teachers’ pedagogical adoption of ICT. In J. Voogt & G. Knezek (Eds.), International handbook of information technology in primary and secondary education (pp. 449–460). New York: Springer.

    Chapter  Google Scholar 

  • Spiteri, M., & Chang Rundgren, S. (2020). Literature review on the factors affecting primary teachers’ use of digital technology. Technology, Knowledge and Learning, 25, 115–128. https://doi.org/10.1007/s10758-018-9376-x.

    Article  Google Scholar 

  • Sullivan, T., Slater, B., Phan, J., Tan, A., & Davis, J. (2019). M-learning: Exploring mobile technologies for secondary and primary school science inquiry. Teaching Science, 65(1), 13–16.

    Google Scholar 

  • R Core Team (2018). R: A language and environment for statistical computing. R Foundation for Statistical Computing. Austria: Vienna. https://www.R-project.org/. Accessed 10 February 2019.

  • Thomas, K., O’Bannon, B., & Britt, V. (2014). Standing in the schoolhouse door: Teacher perceptions of mobile phones in the classroom. Journal of Research on Technology in Education, 46(4), 373–395.

    Article  Google Scholar 

  • UNESCO (2012). Mobile learning for teachers in Europe: exploring the potential of mobile technologies to support teachers and improve practice. Paris 2012. https://unesdoc.unesco.org/ark:/48223/pf0000216167. Accessed 15 December 2018.

  • UNESCO (2013). The future of mobile learning: Implications for policy makers and planners. Paris 2013. http://unesdoc.unesco.org/images/0021/002196/219637e.pdf. Accessed 15 December 2018.

  • Zeng, Y., & Day, C. (2019). Collaborative teacher professional development in schools in England (UK) and Shanghai (China): cultures, contexts and tensions. Teachers and Teaching, 25(3), 379–397.

    Article  Google Scholar 

  • Zhang, Y. (Ed.). (2015). Handbook of mobile teaching and learning. New York: Springer International Publisher.

    Google Scholar 

Download references

Acknowledgements

We would like to thank the teachers who voluntarily participated in the survey, as well as the reviewers and the editor for their constructive feedback.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kleopatra Nikolopoulou.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical Approval

All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendices

Appendix A

See Table 8.

Table 8 Questionnaire [SD: strongly disagree, D: disagree, U: undecided (I am not sure), A: agree, SA: strongly agree]

Appendix B

See Table 9.

Table 9 Pearson product-moment correlations among the remaining 20 items for training and validation samples

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikolopoulou, K., Gialamas, V., Lavidas, K. et al. Teachers’ Readiness to Adopt Mobile Learning in Classrooms: A Study in Greece. Tech Know Learn 26, 53–77 (2021). https://doi.org/10.1007/s10758-020-09453-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-020-09453-7

Keywords

Navigation