Greek Pre-service Teachers’ Intentions to Use Computers as In-service Teachers

Abstract

The study examines the factors affecting Greek pre-service teachers’ intention to use computers when they become practicing teachers. Four variables (perceived usefulness, perceived ease of use, self-efficacy, and attitude toward use) as well as behavioral intention to use computers were used so as to build a research model that extended the Technology Acceptance Model (TAM) and structural equation modeling was used for parameter estimation and model testing. Self-reported data were gathered from 487 pre-service teachers studying at the Departments of Primary School Education in Greece. Results revealed a good model fit and of the nine hypotheses formulated, seven were supported. Overall, the TAM, with the addition of computer self-efficacy beliefs, adequately represented the relationships among the factors. It also possesses the explanatory power to predict pre-service teachers’ intention to use computers when they become practicing teachers since a high percentage (68%) of the variance in behavioral intention to use computers was explained, while the most influential factors were perceived usefulness and attitude toward computers. Implications for practice are also discussed.

Keywords

• attitudes toward computers,perceived ease of use,pre-srvice teachers,self-efficacy,structural equation modelling,technology acceptance model

Kaynakça

1. Ajzen, I. & Fishbein, M. (1980). Understanding attitudes and predicting social behaviour. Englewood Cliffs, NJ: Prentice-Hall.
2. Ajzen, I. & Fishbein, M. (1977). Attitude-behavior relations: A theoretical analysis and review of empirical research. Psychological Bulletin, 84(5), 888-918. doi: 10.1037/0033- 2909.84.5.888
3. Akbulut, Y. (2009). Investigating underlying components of the ICT indicators measurement scale: the extended version. Journal of Educational Computing Research, 40(4), 405-427.
4. Askar, P. & Umay, A. (2001). Pre-service elementary mathematics teachers’ computer selfefficacy, attitudes towards computers, and their perceptions of computer-enriched learning environments. In C. Crawford, et al. (Eds.), Proceedings of Society for Information Technology and Teacher Education International Conference 2001 (pp. 2262-2263). Chesapeake, VA: AACE.
5. Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Prentice-Hall, Inc.
6. Barbeite, F. G. & Weiss, E. M. (2004). Computer self-efficacy and anxiety scales for an Internet sample: testing measurement equivalence of existing measures and development of new scales. Computers in Human Behavior, 20(1), 1-15. doi: 10.1016/S0747- 5632(03)00049-9
7. Bentler, P. M. & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological bulletin, 88(3), 588. doi: 10.1037/0033- 2909.88.3.588
8. Burnham, K. P. & Anderson, D. R. (1998). Model selection and inference: A practical information-theoretic approach. New York: Springer-Verlag. doi: 10.1007/978-1-4757- 2917-7

9. Celik, V. & Yesilyurt, E. (2013). Attitudes to technology, perceived computer self-efficacy, and computer anxiety as predictors of computer supported education. Computers & Education, 60(1), 148-158. doi: 10.1016/j.compedu.2012.06.008
10. Chen, Y. C., Lin, Y. C., Yeh, R. C., & Lou, S. J. (2013). Examining factors affecting college students’ intention to use web-based instruction systems: toward an integrated model. Turkish Online Journal of Educational Technology, 12(2), 111-121.
11. Cheung, W. & Huang, W. (2005). Proposing a framework to assess Internet usage in university education: An empirical investigation from a student’s perspective. British Journal of Educational Technology, 36, 237-253. doi: 10.1111/j.1467-8535.2005.00455.x
12. Chin, W. W. (1988). Issues and opinion on structural equation modeling. MIS Quarterly, 22, viixvi.
13. Chin, W. W., Marcolin, B. L., & Newsted, P. R. (2003). A partial least squares latent variable modeling approach for measuring interaction effects: Results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Information Systems Research, 14(2), 189-217. doi: 10.1287/isre.14.2.189.16018
14. Compeau, D. R. & Higgins, C. A. (1995). Computer self-efficacy: Development of a measure and initial test. MIS Quarterly, 19(2), 189-211. doi: 10.2307/249688
15. Comrey, A. L. & Lee, H. B. (2013). A first course in factor analysis. New York: Psychology Press.
16. Costello, A. B. & Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research & Evaluation, 10(7):1-9.
17. Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions, and behavioral impacts. International Journal of Man-Machine Studies, 38, 475-487. doi: 10.1006/imms.1993.1022
18. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340. doi: 10.2307/249008
19. Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003. doi: 10.1287/mnsc.35.8.982
20. Dawson, J. F. (2014). Moderation in management research: What, why, when, and how. Journal of Business and Psychology, 29(1), 1-19. doi: 10.1007/s10869-013-9308-7
21. DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Newbury Park, CA: SAGE Publications.
22. Everitt, B. S. (1975). Multivariate analysis: The need for data, and other problems. The British Journal of Psychiatry, 126(3), 237-240. doi: 10.1192/bjp.126.3.237
23. Fornell, C., Tellis, G. J., & Zinkhan, G. M. (1982). Validity assessment: A structural equations approach using partial least squares. In B. J. Walker, et al. (Eds.), An assessment of marketing thought & practice (pp. 405-409). Chicago: American Marketing Association.
24. Gaskin, J. (2013). SEM series part 5a: Confirmatory factor analysis. Retrieved on 9 December 2016 from https://www.youtube.com/watch?v=MCYmyzRZnIY Accessed 4/1/2016.
25. Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis: International version (7th Ed). New Jersey: Pearson.
26. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.
27. Hall, G. E. (1979). The concerns-based approach to facilitating change. Educational Horizons, 57, 202–208.
28. Harman, D. (1967). A single factor test of common method variance. Journal of Psychology, 35, 359-378.
29. Hogarty, K. Y., Hines, C. V., Kromrey, J. D., Ferron, J. M., & Mumford, K. R. (2005). The quality of factor solutions in exploratory factor analysis: The influence of sample size, communality, and overdetermination. Educational and Psychological Measurement, 65(2), 202-226. doi: 10.1177/0013164404267287
30. Hsu, M. K., Wang, S. W., & Chiu, K. K. (2009). Computer attitude, statistics anxiety, and selfefficacy on statistical software adoption behaviour: An empirical study of online MBA learners. Computers in Human Behavior, 25, 412-420. doi: 10.1016/j.chb.2008.10.003
31. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling, 6, 1-55. doi: 10.1080/10705519909540118
32. Huang, H. M., & Liaw, S. S. (2005). Exploring user’s attitudes and intentions toward the web as a survey tool. Computers in Human Behavior, 21(5), 729-743. doi: 10.1016/j.chb.2004.02.020
33. Jang, S. J. (2008). The effects of integrating technology, observation and writing into a teacher education method course. Computers & Education, 50(3), 853-865. doi: 10.1016/j.compedu.2006.09.002
34. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement 20, 141-51. doi: 10.1177/001316446002000116
35. Klem, L. (2000). Structural equation modeling. In L. Grimm & P. Yarnold (Eds.), Reading and understanding multivariate statistics, Vol. II. Washington, DC: American Psychological Association.
36. Kline, R. B. (2005). Principles and practice of structural equation modeling (2nd ed.). New York: Guilford Press.
37. Koehler, M., & Mishra, P. (2009). What is technological pedagogical content knowledge (TPACK)? Contemporary Issues in Technology and Teacher Education, 9(1), 60-70.
38. Lai, M. L. (2008). Technology readiness, internet self-efficacy and computing experience of professional accounting students. Campus-Wide Information Systems, 25(1), 18-29. doi: 10.1108/10650740810849061
39. Lowry, P. B., & Gaskin, J. (2014). Partial least squares (PLS) structural equation modeling (SEM) for building and testing behavioral causal theory: When to choose it and how to use it. Professional Communication, IEEE Transactions, 57(2), 123-146. doi: 10.1109/TPC.2014.2312452
40. Luan, W. S., & Teo, T. (2011). Student teachers’ acceptance of computer technology. In T. teo (Ed.), Technology acceptance in education (pp. 43-61). Rotterdam: Sense Publishers. doi: 10.1007/978-94-6091-487-4_3
41. Macharia, J. K. N., & Pelser, T. G. (2012). Key factors that influence the diffusion and infusion of information and communication technologies in Kenyan higher education. Studies in Higher Education. doi: 10.1080/03075079.2012.729033
42. Marakas, G. M., Yi, M. Y., & Johnson, R. D. (1998). The multilevel and multifaceted character of computer self-efficacy: Toward clarification of the construct and an integrative framework for research. Information Systems Research, 9, 126-163. doi: 10.1287/isre.9.2.126
43. Margaryan, A., Littlejohn, A., & Vojt, G. (2011). Are digital natives a myth or reality? University students’ use of digital technologies. Computers & Education, 56(2), 429-440. doi: 10.1016/j.compedu.2010.09.004
44. McDonald, R. P. & Ho, M. R. (2002). Principles and practice in reporting structural equation analyses. Psychological Methods, 7(1), 64-82. doi: 10.1037/1082-989X.7.1.64
45. Mueller, J., Wood, E., Willoughby, T., Ross, C., & Specht, J. (2008). Identifying discriminating variables between teachers who fully integrate computers and teachers with limited integration. Computers & Education, 51(4), 1523-1537. doi: 10.1016/j.compedu.2008.02.003
46. O'Brien, R. M. (2007). A caution regarding rules of thumb for variance inflation factors. Quality & Quantity, 41(5), 673-690. doi: 10.1007/s11135-006-9018-6
47. Ottenbreit-Leftwich, A. T., Glazewski, K. D., Newby, T. J., & Ertmer, P. A. (2010). Teacher value beliefs associated with using technology: Addressing professional and student needs. Computers & Education, 55(3), 1321-1335. doi: 10.1016/j.compedu.2010.06.002
48. Paraskeva, F., Bouta, H., & Papagianni, A. (2008). Individual characteristics and computer selfefficacy in secondary education teachers to integrate technology in educational practice. Computers & Education, 50(3), 1084-1091. doi: 10.1016/j.compedu.2006.10.006
49. Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879. doi: 10.1037/0021-9010.88.5.879
50. Preacher, K. J. & Hayes, A. F. (2008). Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods, 40(3), 879-891. doi: 10.3758/BRM.40.3.879
51. Rauniar, R., Rawski, G., Yang, J., & Johnson, B. (2014). Technology acceptance model (TAM) and social media usage: An empirical study on Facebook. Journal of Enterprise Information Management, 27(1), 6-30. doi: 10.1108/JEIM-04-2012-0011
52. Rogers, E. (1995). Diffusion of innovations (4th ed.). New York: Free Press.
53. Schumacker, R. E., & Lomax, R. G. (2010). A beginner' guide to structural equation modeling (3rd ed.). New York: Routledge.
54. Schoolnet, E. (2013). Survey of schools: ICT in education. Benchmarking access, use and attitudes to technology in European schools. Liége: European Union.
55. Selwyn, N. (1997). Students' attitudes toward computers: Validation of a computer attitude scale for 16-19 education. Computers & Education, 28, 35-41. doi: 10.1016/S0360- 1315(96)00035-8
56. Tabachnick B. G., & Fidell L. S. (2007). Using multivariate statistics. Boston: Pearson. Taylor, S. & Todd, P. A. (1995). Understanding information technology usage: A test of competing models. Information Systems Research, 6(2), 144-176. doi: 10.1287/isre.6.2.144
57. Teo, T. (2014). Unpacking teachers’ acceptance of technology: Tests of measurement invariance and latent mean differences. Computers & Education, 75, 127-135. doi: 10.1016/j.compedu.2014.01.014
58. Teo, T. (2010). Explaining the intention to use technology among volitional users in education: An evaluation of the Technology Acceptance Model (TAM) using structural equation modeling. International Journal of Instructional Media, 37(4), 379-389.
59. Teo, T. (2009). Modelling technology acceptance in education: A study of pre-service teachers. Computers & Education, 52(1), 302-312. doi: 10.1016/j.compedu.2008.08.006
60. Teo, T. & Noyes, J. (2011). An assessment of the influence of attitude and perceived enjoyment on the intention to use technology among pre-service teachers: A structural equation modelling approach. Computers & Education, 57(2), 1645-1653. doi: 10.1016/j. compedu.2011.03.002
61. Teo, T. & Zhou, M. (2014). Explaining the intention to use technology among university students: a structural equation modeling approach. Journal of Computing in Higher Education, 26(2), 124-142. doi: 10.1007/s12528-014-9080-3
62. Tung, F. C., & Chang, S. C. (2008). Nursing students’ behavioural intention to use online courses: A questionnaire survey. International Journal of Nursing Studies, 45, 1299-1309. doi: 10.1016/j.ijnurstu.2007.09.011
63. Venkatesh, V., Morris, M., Davis, G., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425-478.
64. Wallace, L. G., & Sheetz, S. D. (2014). The adoption of software measures: A technology acceptance model (TAM) perspective. Information and Management, 51, 249-259. doi: 10.1016/j.im.2013.12.003
65. Wong, K. T., Teo, T., & Russo, S. (2013). Interactive whiteboard acceptance: Applicability of the UTAUT model among student teachers. The Asia Pacific Education Researcher, 22(1), 1- 10. doi: 10.1007/s40299-012-0001-9