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An initial development and validation of a Chinese technology teachers’ attitudes towards technology (TTATT) scale

  • Meidan Xu
  • John P. Williams
  • Jianjun GuEmail author
Article
  • 29 Downloads

Abstract

Pupils’ attitudes towards technology have been widely discussed for over three decades, but the equally important topic of teachers’ attitudes towards technology has not gained similar attention. To address this gap, the technology teachers’ attitude towards technology (TTATT) scale was developed and validated by pilot testing with 140 Chinese high school general technology teachers. The theoretical framework of the TTATT scale was based on the tripartite model of teachers’ attitude towards science, resulting in 23 test items. The results of reliability analysis demonstrated the TTATT scale is reliable with this sample of Chinese technology teachers, and the results of exploratory factor analysis and confirmatory factor analysis indicated that the seven-dimension TTATT scale is consistent with hypothetical theoretical constructs. The seven dimensions are: relevance, difficulty, gender beliefs, enjoyment, anxiety, self-efficacy, and context dependency. The findings demonstrated that the TTATT scale is based on a sound conceptual foundation, and has good construct and factorial validity. The study advances the definition of attitude towards technology and provides a good template for developing a new attitude scale in different discipline contexts.

Keywords

Technology teachers Attitudes towards technology Technology education Scale development and validation 

Notes

Acknowledgements

This work was supported by the (Funding: the Priority Academic Program Development of Jiangsu Higher Education Institutions) under Grant (No. 164320H111).

References

  1. Aalderen-Smeets, S. I. V., Molen, J. H. W. V. D., & Asma, L. J. F. (2012). Primary teachers’ attitudes toward science: A new theoretical framework. Science Education,96(1), 158–182.Google Scholar
  2. Ajzen, I. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.Google Scholar
  3. Ajzen, I. (2001). Nature and operation of attitudes. Annual Review of Psychology,52(1), 27–58.Google Scholar
  4. Androulidakis, S. (1991). Greek teachers’ attitudes towards technology, a first approach. In PATT 5 conference proceedings (pp. 219–223). Eindhoven, The Netherlands.Google Scholar
  5. Ankiewicz, P. (2019a). Perceptions and attitudes of pupils towards technology: In search of a rigorous theoretical framework. International Journal of Technology and Design Education,29(1), 37–56.Google Scholar
  6. Ankiewicz, P. (2019b). Alignment of the traditional approach to perceptions and attitudes with Mitcham’s philosophical framework of technology. International Journal of Technology and Design Education,29(2), 329–340.Google Scholar
  7. Ankiewicz, P., Van Rensburg, S., & Myburgh, C. (2001). Assessing the attitudinal technology profile of South African learners: A pilot study. International Journal of Technology and Design Education,11(2), 93–109.Google Scholar
  8. Ardies, J., Maeyer, S. D., & Gijbels, D. (2013). Reconstructing the pupils attitude towards technology-survey. Design and Technology Education,18(1), 8–19.Google Scholar
  9. Ardies, J., Maeyer, S. D., Gijbels, D., & Keulen, H. V. (2015). Students attitudes towards technology. International Journal of Technology and Design Education,25(1), 43–65.Google Scholar
  10. Asma, L., Molen, J. W. V. D., & Aalderen-Smeets, S. V. (2011). Primary teachers’ attitudes towards science and technology: Results of a focus group study. Professional development for primary teachers in science and technology (pp. 89–105). Rotterdam: SensePublishers.Google Scholar
  11. Bame, E. A. (1989). What do American teachers think of technology. In PATT 4 conference proceedings (pp. 320–323). Eindhoven, The Netherlands.Google Scholar
  12. Bame, E. A., Dugger, W., De Vries, M. J., & McBee, J. (1993). Pupils’ attitudes toward technology˗PATT-USA. Journal of Technology Studies, 19(1), 40–48.Google Scholar
  13. Bandura, A. (1997). Self-efficacy: The exercise of control. New York: Freeman.Google Scholar
  14. Becker, K. H., & Maunsaiyat, S. (2002). Thai students’ attitudes and concepts of technology. Journal of Technology Education,13(2), 6–19.Google Scholar
  15. Bentler, P. M. (1990). Comparative fit indexes in structural models. Psychological Bulletin,107(2), 238–246.Google Scholar
  16. Bentler, P. M., & Bonett, D. G. (1980). Significance tests and goodness of fit in the analysis of covariance structures. Psychological Bulletin,88(3), 588–606.Google Scholar
  17. Bilgin, H., & Öznacar, B. (2017). Development of the attitude scale towards crisis and chaos management in education. Eurasia Journal of Mathematics, Science and Technology Education,13(11), 7381–7389.Google Scholar
  18. Can, A. (2014). Quantitative data analysis in the scientific research process with SPSS. Ankara: PegemA Publishing.Google Scholar
  19. De Klerk Wolters, F. (1988). PATT research in 1987/88. In PATT 3 conference proceedings (pp. 39–46). Eindhoven, The Netherlands.Google Scholar
  20. De Klerk Wolters, F. (1989). A PATT study among 10 to 12-year-olds. In PATT 4 conference proceddings (pp. 324–330). Eindhoven, The Netherlands.Google Scholar
  21. Eagly, A. H., & Chaiken, S. (1993). The psychology of attitudes. Orlando, US: Harcourt Brace Jovanovich College Publishers.Google Scholar
  22. Edwards, A. L. (1957). Techniques of attitude scale construction. Englewood-Cliffs, NJ: Prentice-Hall.Google Scholar
  23. Field, A. (2013). Discovering statistics using IBM SPSS statistics. Thousand Oaks, CA: SAGE Publications.Google Scholar
  24. Ford, M. E. (1992). Motivating humans: Goals, emotions, and personal agency beliefs. Thousand Oaks, CA: SAGE Publications.Google Scholar
  25. Gu, J. J. (2015). Analysis and reflection on the achievements and problems of General Technology curriculum experiment in high school in China during the past 10 years. Educational Research and experiment,2, 29–36.Google Scholar
  26. Hassan, A. M. A., & Shrigley, R. L. (1984). Designing a likert scale to measure chemistry attitudes. School Science and Mathematics,84(8), 659–669.Google Scholar
  27. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Evaluating model fit: A synthesis of the structural equation modeling literature. In Paper present at the 7 thEuropean conference on research methodology for business and management studies. Regent’s College, London, United Kingdom.Google Scholar
  28. Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal,6(1), 1–55.Google Scholar
  29. Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement,20(1), 141–151.Google Scholar
  30. Kline, R. B. (2005). Principle and practice of structural equation modeling (2nd ed.). New York: The Guilford Press.Google Scholar
  31. Mottier, I., Raat, J. H., & De Vries, M. J. (1991). Research. In PATT 5 conference proceedings (pp. 29–36). Eindhoven, The Netherlands.Google Scholar
  32. Nordlöf, C., Höst, G. E., & Hallström, J. (2017). Swedish technology teachers’ attitudes to their subject and its teaching. Research in Science & Technological Education,35(2), 195–214.Google Scholar
  33. Nordlöf, C., Hallström, J., & Höst, G. E. (2019). Self-efficacy or context dependency?: Exploring teachers’ perceptions of and attitudes towards technology education. International Journal of Technology and Design Education,29(1), 123–141.Google Scholar
  34. Osborne, J. W., Costello, A. B., & Jason, W. O. (2008). Best practices in exploratory factor analysis. In J. W. Osborne (Ed.), Best practices in quantitative methods (pp. 86–99). Thousand Oaks, CA: SAGE Publications.Google Scholar
  35. Raat, J. H., & De Vries, M. J. (1985). What do 13-year old students think about technology? The conception of and the attitude towards technology of 13-year old girls and boys. Eindhoven University of Technology, The Netherlands (ERIC Number: ED262998).Google Scholar
  36. Rohaan, E. J., Taconis, R., & Jochems, W. M. G. (2010). Reviewing the relations between teachers’ knowledge and pupils’ attitude in the field of primary technology education. International Journal of Technology and Design Education,20(1), 15–26.Google Scholar
  37. Steiger, J. H. (1990). Structural model evaluation and modification: An interval estimation approach. Multivariate Behavioral Research,25(2), 173–180.Google Scholar
  38. Suprapto, N., & Mursid, A. (2017). Pre-service teachers’ attitudes toward teaching science and their science learning at Indonesia open university. Turkish Online Journal of Distance Education,18(4), 66–77.Google Scholar
  39. Tabachnick, B. G., Fidell, L. S., & Ullman, J. B. (2007). Using multivariate statistics (Vol. 5). Boston, MA: Pearson.Google Scholar
  40. Thibaut, L., Knipprath, H., Dehaene, W., & Depaepe, F. (2018). How school context and personal factors relate to teachers’ attitudes toward teaching integrated STEM. International Journal of Technology and Design Education,28(3), 631–651.Google Scholar
  41. Van Rensburg, S., Ankiewicz, P., & Myburgh, C. (1999). Assessing South Africa learners’ attitudes towards technology by using the PATT (Pupils’ Attitudes towards Technology) questionnaire. International Journal of Technology and Design Education,9(2), 137–151.Google Scholar
  42. Volk, K. S., & Yip, W. M. (1999). Gender and technology in Hong Kong: A study of pupils’ attitudes toward technology. International Journal of Technology and Design Education,9(1), 57–71.Google Scholar
  43. Wu, M. R. (2010). Structural equation model: Operation and application of AMOS. Chongqing: Chongqing University Press.Google Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.School of Education ScienceNanjing Normal UniversityNanjingChina
  2. 2.Curtin UniversityPerthAustralia

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