Factors impacting special education teachers’ acceptance and actual use of technology

  • Nur SiyamEmail author


This study uses the Technology Acceptance Model (TAM) to explore the factors that impact special education teachers’ acceptance and actual use of technology. TAM is used as the foundation for generating the hypothesis and developing the conceptual framework for the study. Twenty-four (n=24) special education teachers in a private school in the United Arab Emirates (UAE) participated in this study by answering an electronic questionnaire that included items related to Perceived Usefulness, Perceived Ease of Use, Attitudes Towards Usage, Behavioural Intention to Use, Access to Technology, Job Relevance, Self-Efficacy, Time, and Actual Usage. Preliminary findings indicate that special education teachers have positive attitudes towards the use of technology. Self-efficacy, time and access to technology were found to significantly impact actual use of technology. The research results provide initial insights on special education teachers attitudes towards using technology in their practice as well as the factors that may facilitate or hinder their actual use. Implications for practice and future research are discussed.


Technology Acceptance Model (TAM) Special Education Teachers Technology Usage Attitudes 



  1. Alharbi, S., & Drew, S. (2014). Using the technology acceptance model in understanding academics’ behavioural intention to use learning management systems. International Journal of Advanced Computer Science and Applications, 5(1), 143–155.Google Scholar
  2. Almeida, C. M., Jameson, J. M., Riesen, T., & McDonnell, J. (2016). Urban and rural preservice special education teachers' computer use and perceptions of self-efficacy. Rural Special Education Quarterly, 35(3), 12–19.Google Scholar
  3. Alshammari, S. H., Ali, M. B., & Rosli, M. S. (2016). The Influences of Technical Support, Self-Efficacy and Instructional Design on the Usage and Acceptance of LMS: A Comprehensive Review. Turkish Online Journal of Educational Technology-TOJET, 15(2), 116–125.Google Scholar
  4. Baglama, B., Yikmis, A., & Demirok, M. S. (2017). Special education teachers’ views on using technology in teaching mathematics. European Journal of Special Education Research.Google Scholar
  5. Bagozzi, R. P. (2007). The legacy of the technology acceptance model and a proposal for a paradigm shift. Journal of the association for information systems, 8(4), 3.Google Scholar
  6. Bahr, D. L., Shaha, S. H., Farnsworth, B. J., Lewis, V. K., & Benson, L. F. (2004). Preparing Tomorrow's Teachers to Use Technology: Attitudinal Impacts of Technology-supported Field Experience On Pre-service Teacher Candidates. Journal of Instructional Psychology, 31(2).Google Scholar
  7. Bandura, A. (1977). Self-efficacy: toward a unifying theory of behavioral change. Psychological review, 84(2), 191.Google Scholar
  8. Baturay, M. H., Gökçearslan, Ş., & Ke, F. (2017). The relationship among pre-service teachers' computer competence, attitude towards computer-assisted education, and intention of technology acceptance. International Journal of Technology Enhanced Learning, 9(1), 1–13.Google Scholar
  9. Benbasat, I., & Barki, H. (2007). Quo vadis TAM? Journal of the Association for Information Systems, 8(4), 7.Google Scholar
  10. Birkollu, S. S., Yucesoy, Y., Baglama, B., & Kanbul, S. (2017). Investigating the Attitudes of Preservice Teachers Towards Technology Based on Various Variables. TEM Journal, 6(3), 578–583.Google Scholar
  11. Bruhn, A. L., Woods-Groves, S., Fernando, J., Choi, T., & Troughton, L. (2017). Evaluating technology-based self-monitoring as a tier 2 intervention across middle school settings. Behavioral Disorders, 42(3), 119–131.Google Scholar
  12. Buabeng-Andoh, C. (2012). Factors influencing teachers' adoption and integration of information and communication technology into teaching: A review of the literature. International Journal of Education and Development using Information and Communication Technology, 8(1), 136.Google Scholar
  13. Cafiero, J. M. (2012). Technology supports for individuals with autism spectrum disorders. Journal of Special Education Technology, 27(1), 64–76.Google Scholar
  14. Chang, J. L., Lieu, P. T., Liang, J. H., Liu, H. T., & Wong, S. L. (2011). Factors influencing technology acceptance decisions. African Journal of Business Management, 5(7), 2901–2909.Google Scholar
  15. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behavior. Computers & Education, 59(3), 1054–1064.Google Scholar
  16. Conti, D., Commodari, E., & Buono, S. (2017). Personality factors and acceptability of socially assistive robotics in teachers with and without specialized training for children with disability. Life Span and Disability, 20(2), 251–272.Google Scholar
  17. Creswell, J. W. (2014). Educational Research: Planning, Conducting, and Evaluating Quantitative and Qualitative Research. Pearson publications.Google Scholar
  18. Crutchfield, S. A., Mason, R. A., Chambers, A., Wills, H. P., & Mason, B. A. (2015). Use of a self-monitoring application to reduce stereotypic behavior in adolescents with autism: A preliminary investigation of I-Connect. Journal of Autism and Developmental Disorders, 45(5), 1146–1155.Google Scholar
  19. Davis, F. D. (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. MIS Quarterly, 13(3), 319–339.Google Scholar
  20. El Alfy, S., Gómez, J. M., & Ivanov, D. (2017). Exploring instructors’ technology readiness, attitudes and behavioral intentions towards e-learning technologies in Egypt and United Arab Emirates. Education and Information Technologies, 22(5), 2605–2627.Google Scholar
  21. 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.Google Scholar
  22. Fullan, M. (2001). The new meaning of educational change (3rd ed.). New York: Teachers College Press.Google Scholar
  23. Gorsuch, R. L. (1988). Exploratory factor analysis. In Handbook of multivariate experimental psychology (pp. 231–258). Boston: Springer.Google Scholar
  24. Guieford, J. P. (1965). Fundamental Statistics in Psychology and Education. New York: McGram-Hill.Google Scholar
  25. Holden, H., & Rada, R. (2011). Understanding the influence of perceived usability and technology self-efficacy on teachers’ technology acceptance. Journal of Research on Technology in Education, 43(4), 343–367.Google Scholar
  26. Ismail, S. A. A., Almekhlafi, A. G., & Al-Mekhlafy, M. H. (2010). Teachers’ perceptions of the use of technology in teaching languages in United Arab Emirates’ schools. International Journal for Research in Education, 27(1), 37–56.Google Scholar
  27. Juhary, J. (2014). Perceived usefulness and ease of use of the learning management system as a learning tool. International Education Studies, 7(8), 23.Google Scholar
  28. Ke, F., & Moon, J. (2018). Virtual collaborative gaming as social skills training for high-functioning autistic children. British Journal of Educational Technology, 49(4), 728–741.Google Scholar
  29. Kopcha, T. J. (2012). Teachers' perceptions of the barriers to technology integration and practices with technology under situated professional development. Computers & Education, 59(4), 1109–1121.Google Scholar
  30. Koutromanos, G., Styliaras, G., & Christodoulou, S. (2015). Student and in-service teachers’ acceptance of spatial hypermedia in their teaching: The case of HyperSea. Education and Information Technologies, 20(3), 559–578.Google Scholar
  31. Leong, L. W., Ibrahim, O., Dalvi-Esfahani, M., Shahbazi, H., & Nilashi, M. (2018). The moderating effect of experience on the intention to adopt mobile social network sites for pedagogical purposes: An extension of the technology acceptance model. Education and Information Technologies, 23(6), 2477–2498.Google Scholar
  32. Litz, D., & Scott, S. (2017). Transformational leadership in the educational system of the United Arab Emirates. Educational Management Administration & Leadership, 45(4), 566–587.Google Scholar
  33. Liu, S. H. (2012). A multivariate model of factors influencing technology use by preservice teachers during practice teaching. Journal of Educational Technology & Society, 15(4), 137.Google Scholar
  34. Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81.Google Scholar
  35. Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of perceived user resources. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 32(3), 86–112.Google Scholar
  36. Narkon, D. E., Wells, J. C., & Segal, L. S. (2011). E-word wall: An interactive vocabulary instruction tool for students with learning disabilities and autism spectrum disorders. Teaching Exceptional Children, 43(4), 38–45.Google Scholar
  37. O’Malley, P., Lewis, M. E. B., Donehower, C., & Stone, D. (2014). Effectiveness of using iPads to increase academic task completion by students with autism. Universal Journal of Educational Research, 2(1), 90–97.Google Scholar
  38. Okolo, C. M., & Diedrich, J. (2014). Twenty-five years later: How is technology used in the education of students with disabilities? Results of a statewide study. Journal of Special Education Technology, 29(1), 1–20.Google Scholar
  39. Oye, N. D., Iahad, N. A., & Rahim, N. A. (2014). The history of UTAUT model and its impact on ICT acceptance and usage by academicians. Education and Information Technologies, 19(1), 251–270.Google Scholar
  40. Parkman, S., Litz, D., & Gromik, N. (2018). Examining pre-service teachers’ acceptance of technology-rich learning environments: A UAE case study. Education and Information Technologies, 23(3), 1253–1275.Google Scholar
  41. Saddler, B., Newman, D., & Passa, K. (2006, March). The Benefits of Integrating Technology Into Inclusive Classrooms. In Society for Information Technology & Teacher Education International Conference (pp. 4225–4230). Association for the Advancement of Computing in Education (AACE).Google Scholar
  42. Sahin, A., Top, N., & Delen, E. (2016). Teachers’ first-year experience with chromebook laptops and their attitudes towards technology integration. Technology, Knowledge and Learning, 21(3), 361–378.Google Scholar
  43. Schmidt, M. M. (2014). Designing for learning in a three-dimensional virtual learning environment: A design-based research approach. Journal of Special Education Technology, 29(4), 59–71.Google Scholar
  44. Siegel, J., Good, K., & Moore, J. (1996). Integrating technology into educating preservice special education teachers. Action in Teacher Education, 17(4), 53–63.Google Scholar
  45. Siyam N. (2018, in press). Special Education Teachers’ Perceptions on Using Technology for Communication Practices. Journal for Researching Education Practice and Theory (JREPT), 1(2), 1–16.Google Scholar
  46. Stockless, A. (2018). Acceptance of learning management system: The case of secondary school teachers. Education and Information Technologies, 23(3), 1101–1121.Google Scholar
  47. Teo, T. (2008). Pre-service teachers' attitudes towards computer use: A Singapore survey. Australasian Journal of Educational Technology, 24(4).Google Scholar
  48. Teo, T. (2009). Examining the Relationship between Student Teachers' Self-Efficacy Beliefs and Their Intended Uses of Technology for Teaching: A Structural Equation Modelling Approach. Turkish Online Journal of Educational Technology-TOJET, 8(4), 7–15.Google Scholar
  49. Teo, T. (2011a). Factors influencing teachers’ intention to use technology: Model development and test. Computers & Education, 57(4), 2432–2440.Google Scholar
  50. Teo, T. (Ed.). (2011b). Technology acceptance in education. Springer Science & Business Media.Google Scholar
  51. Teo, T., Faruk Ursavaş, Ö., & Bahçekapili, E. (2011). Efficiency of the technology acceptance model to explain pre-service teachers' intention to use technology: A Turkish study. Campus-Wide Information Systems, 28(2), 93–101.Google Scholar
  52. Tyler-Wood, T. L., Putney, D., & Cass, M. A. (1997). Accessibility: the main factor influencing special education teachers’ perceived level of computer competence. Journal of Computing in Teacher Education, 13(4), 20–24.Google Scholar
  53. Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186–204.Google Scholar
  54. Wu, C., & Liu, C. F. (2015). Acceptance of ICT-mediated teaching/learning systems for elementary school teachers: Moderating effect of cognitive styles. Education and Information Technologies, 20(2), 381–401.MathSciNetGoogle Scholar
  55. Xin, Y. P., Tzur, R., Hord, C., Liu, J., Park, J. Y., & Si, L. (2017). An intelligent tutor-assisted mathematics intervention program for students with learning difficulties. Learning Disability Quarterly, 40(1), 4–16.Google Scholar
  56. Zhao, Y., Tan, S. H., & Mishra, P. (2001). Teaching and learning: Whose computer is it? Journal of Adolescent & Adult Literacy, 44(4), 348.Google Scholar

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

Authors and Affiliations

  1. 1.The British University in DubaiDubaiUnited Arab Emirates

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