Advertisement

Identifying Taiwanese Teachers’ Perceived Self-efficacy for Science, Technology, Engineering, and Mathematics (STEM) Knowledge

  • Min-Hsien Lee
  • Chung-Yuan Hsu
  • Chun-Yen Chang
Regular Article

Abstract

This study, first of all, aimed to develop a new survey to assess Taiwanese teachers’ perceived self-efficacy in STEM knowledge. Second, it aimed to probe any differences in teachers’ perceived self-efficacy in STEM knowledge regarding their gender and teaching subjects. Last, we examined the structural relations among teachers’ perceived self-efficacy in STEM knowledge and their attitudes toward STEM education. The participants were 220 high school teachers in Taiwan. The 30-item instrument consisted of six factors: scientific inquiry, technology use, engineering design, mathematical thinking, and synthesized knowledge of STEM, as well as attitudes toward STEM education. The results showed that the proposed instrument was valid and reliable. In addition, male teachers outperformed female teachers in each dimension of the survey. Last, teachers’ self-efficacy in synthesized knowledge of STEM had two mediating effects. One was in the relationship between self-efficacy in engineering design and attitudes toward STEM education. The other was in the relationship between self-efficacy in Mathematical Thinking and Attitudes toward STEM education.

Keywords

STEM Teacher education Teacher knowledge Attitudes toward STEM Self-efficacy 

Notes

Acknowledgements

This work was financially supported by the “Institute for Research Excellence in Learning Sciences” of National Taiwan Normal University (NTNU) from The Featured Areas Research Center Program within the framework of the Higher Education Sprout Project by the Ministry of Education (MOE) in Taiwan and was also supported by the Ministry of Science and Technology, Taiwan, under Grant Contract Numbers 106-2628-S-003 -002 -MY3 and 106-2628-S-020-001-MY3.

References

  1. Bagozzi, R. P., & Yi, Y. (1988). On the evaluation of structural equation models. Academic of Marketing Science, 16(1), 76–94.CrossRefGoogle Scholar
  2. Bandura, A., Barbaranelli, C., Caprara, G. V., & Pastorelli, C. (1996). Multifaceted impact of self-efficacy beliefs on academic functioning. Child Development, 67(3), 1206–1222.  https://doi.org/10.1111/j.1467-8624.1996.tb01791.x.CrossRefGoogle Scholar
  3. Bong, M. (1999). Personal factors affecting the generality of academic self-efficacy judgments: Gender, ethnicity, and relative expertise? The Journal of Experimental Education, 67(4), 315–331.  https://doi.org/10.1037/e527772014-429.CrossRefGoogle Scholar
  4. Bottia, M. C., Stearns, E., Mickelson, R. A., Moller, S., & Valentino, L. (2015). Growing the roots of STEM majors: Female math and science high school faculty and the participation of students in STEM. Economics of Education Review, 45, 14–27.  https://doi.org/10.1016/j.econedurev.2015.01.002.CrossRefGoogle Scholar
  5. Chai, C. S., Koh, J. H. L., & Teo, Y. H. (2018). Enhancing and modeling teachers’ design beliefs and efficacy of technological pedagogical content knowledge for 21st century quality learning. Journal of Educational Computing Research.  https://doi.org/10.1177/0735633117752453.Google Scholar
  6. Chai, C. S., Koh, J. H. L., & Tsai, C.-C. (2013). Facilitating preservice teachers’ development of Technological, Pedagogical, and Content Knowledge (TPACK). Educational Technology & Society, 13, 63–73.Google Scholar
  7. D’Angelo, C., Rutstein, D., Harris, C., Bernard, R., Borokhovski, E., & Haertel, G. (2014). Simulations for STEM learning: Systematic review and meta-analysis. Menlo Park: SRI International.Google Scholar
  8. DeCoito, I., & Myszkal, P. (2018). Connecting science instruction and teachers’ self-efficacy and beliefs in STEM education. Journal of Science Teacher Education.  https://doi.org/10.1080/1046560X.2018.1473748.Google Scholar
  9. Donna, J. D. (2012). A model for professional development to promote engineering design as an integrative pedagogy within STEM education. Journal of Pre-College Engineering Education Research.  https://doi.org/10.5703/1288284314866.Google Scholar
  10. Driskell, N. (2014). Global perspectives: Explaining Taiwan’s dramatic improvement in PISA reading. Washington, DC: NCEE (National Center on Education and The Economy). Retrieved from http://ncee.org/2014/10/global-perspectives-explaining-taiwans-dramatic-improvement-in-pisa-reading/.
  11. English, L. D. (2016). STEM education K-12: Perspectives on integration. International Journal of STEM Education, 3(1), 3.  https://doi.org/10.1186/s40594-016-0036-1.CrossRefGoogle Scholar
  12. English, L. D., & King, D. T. (2015). STEM learning through engineering design: Fourth-grade students’ investigations in aerospace. International Journal of STEM Education, 2(1), 14.  https://doi.org/10.1186/s40594-015-0027-7.CrossRefGoogle Scholar
  13. Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39–50.CrossRefGoogle Scholar
  14. Griffith, A. L. (2014). Faculty gender in the college classroom: Does it matter for achievement and major choice? Southern Economic Journal, 81(1), 211–231.  https://doi.org/10.4284/0038-4038-2012.100.CrossRefGoogle Scholar
  15. Guzey, S. S., Moore, T. J., & Harwell, M. (2016). Building up STEM: An analysis of teacher-developed engineering design-based STEM integration curricular materials. Journal of Pre-College Engineering Education Research.  https://doi.org/10.7771/2157-9288.1129.Google Scholar
  16. Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate data analysis (6th ed.). New York: Prentice Hall.Google Scholar
  17. Honey, M., Pearson, G., & Schweingruber, H. (Eds.). (2014). STEM integration in K–12 education: Status, prospects, and an agenda for research. Washington, DC: National Academies Press.Google Scholar
  18. Joreskog, K. G., & Sorbom, D. (1986). LISREL VI: Analysis of linear structural relationships by maximum likelihood, instrumental variables, and least squares methods (4th ed.). Mooresville, IN: Scientific Software.Google Scholar
  19. Kelley, T. R., & Knowles, J. G. (2016). A conceptual framework for integrated STEM education. International Journal of STEM Education, 3(1), 11.  https://doi.org/10.1186/s40594-016-0046-z.CrossRefGoogle Scholar
  20. Lee, M.-H., & Tsai, C.-C. (2010). Exploring teachers’ perceived self-efficacy and technological pedagogical content knowledge with respect to educational use of the World Wide Web. Instructional Science, 38, 1–21.CrossRefGoogle Scholar
  21. Leel, H.-C. (2011). In defense of concordancing: An application of data-driven learning in Taiwan. Procedia - Social and Behavioral Sciences, 12, 399–408.  https://doi.org/10.1016/j.sbspro.2011.02.049.CrossRefGoogle Scholar
  22. Lin, T.-C., Tsai, C.-C., Chai, C. S., & Lee, M.-H. (2013). Identifying science teachers’ perceptions of technological, pedagogical, and content knowledge (TPACK). Journal of Science Education and Technology, 22, 325–336.CrossRefGoogle Scholar
  23. Lin, K.-Y., & Williams, P. J. (2016). Taiwanese preservice teachers’ science, technology, engineering, and mathematics teaching intention. International Journal of Science and Mathematics Education, 14(6), 1021–1036.  https://doi.org/10.1007/s10763-015-9645-2.CrossRefGoogle Scholar
  24. Ministry of Education. (2018). Grade 1-12 curriculum guidelines. Retrieved from https://www.edu.tw/News_Content.aspx?n=D33B55D537402BAA&s=37E2FF8B7ACFC28B.
  25. Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108, 1017–1054.CrossRefGoogle Scholar
  26. Nadelson, L. S., Callahan, J., Pyke, P., Hay, A., Dance, M., & Pfiester, J. (2013). Teacher STEM perception and preparation: Inquiry-based STEM professional development for elementary teachers. The Journal of Educational Research, 106(2), 157–168.  https://doi.org/10.1080/00220671.2012.667014.CrossRefGoogle Scholar
  27. National Academy of Engineering and National Research Council. (2014). STEM integration in K–12 education: Status, prospects, and an agenda for research. Washington, DC: The National Academies Press.Google Scholar
  28. National Research Council. (2012). A framework for K12 science education: Practices, cross cutting concepts, and core ideas. Washington: National Academies Press.Google Scholar
  29. OECD. (2016). PISA 2015 results (volume I): Excellence and equity in education. Paris: PISA, OECD Publishing.  https://doi.org/10.1787/9789264266490-en.Google Scholar
  30. 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, 879–891.  https://doi.org/10.3758/BRM.40.3.879.CrossRefGoogle Scholar
  31. Price, J. (2010). The effect of instructor race and gender on student persistence in STEM fields. Economics of Education Review, 29(6), 901–910.  https://doi.org/10.1016/j.econedurev.2010.07.009.CrossRefGoogle Scholar
  32. Shernoff, D. J., Sinha, S., Bressler, D. M., & Ginsburg, L. (2017). Assessing teacher education and professional development needs for the implementation of integrated approaches to STEM education. International Journal of STEM Education.  https://doi.org/10.1186/s40594-017-0068-1.Google Scholar
  33. Shrout, P. E., & Bolger, N. (2002). Mediation in experimental and nonexperimental studies: New procedures and recommendations. Psychological Methods, 7, 422–445.CrossRefGoogle Scholar
  34. Swaid, S. I. (2015). Bringing computational thinking to STEM education. Procedia Manufacturing, 3, 3657–3662.  https://doi.org/10.1016/j.promfg.2015.07.761.CrossRefGoogle Scholar
  35. Thibaut, L., Knipprath, H., Dehaene, W., & Depaepe, F. (2018a). Teachers’ attitudes toward teaching integrated stem: The impact of personal background characteristics and school context. International Journal of Science and Mathematics Education.  https://doi.org/10.1007/s10763-018-9898-7.Google Scholar
  36. Thibaut, L., Knipprath, H., Dehaene, W., & Depaepe, F. (2018b). The influence of teachers’ attitudes and school context on instructional practices in integrated STEM education. Teaching and Teacher Education, 71, 190–205.  https://doi.org/10.1016/j.tate.2017.12.014.CrossRefGoogle Scholar
  37. Uitto, A. (2014). Interest, attitudes and self-efficacy beliefs explaining upper-secondary school students’ orientation towards biology-related careers. International Journal of Science and Mathematics Education, 12(6), 1425–1444.CrossRefGoogle Scholar
  38. van Aalderen-Smeets, S., & Walma van der Molen, J. (2013). Measuring primary teachers’ attitudes toward teaching science: Development of the dimensions of attitude toward science (DAS) instrument. International Journal of Science Education, 35(4), 577–600.  https://doi.org/10.1080/09500693.2012.755576.CrossRefGoogle Scholar
  39. Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., et al. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.  https://doi.org/10.1007/s10956-015-9581-5.CrossRefGoogle Scholar

Copyright information

© De La Salle University 2018

Authors and Affiliations

  1. 1.Program of Learning Sciences and Institute for Research Excellence in Learning SciencesNational Taiwan Normal UniversityTaipeiTaiwan
  2. 2.Department of Child CareNational Pingtung University of Science and TechnologyPingtungTaiwan
  3. 3.Graduate Institute of Science Education and Institute for Research Excellence in Learning SciencesNational Taiwan Normal UniversityTaipeiTaiwan

Personalised recommendations