Skip to main content

Advertisement

Log in

Taiwanese Preservice Teachers’ Science, Technology, Engineering, and Mathematics Teaching Intention

  • Published:
International Journal of Science and Mathematics Education Aims and scope Submit manuscript

Abstract

This study applies the theory of planned behavior as a basis for exploring the impact of knowledge, values, subjective norms, perceived behavioral controls, and attitudes on the behavioral intention toward science, technology, engineering, and mathematics (STEM) education among Taiwanese preservice science teachers. Questionnaires (N = 139) collected information on the behavioral intention of preservice science teachers engaging in STEM education. Data were analyzed using descriptive statistics, path analysis, and analysis of variance. Results revealed that, in terms of direct effects, higher perceived behavioral control and subjective norms were associated with stronger STEM teaching intention. More positive attitude and greater knowledge were indirectly associated with higher subjective norms and perceived behavioral control, which resulted in stronger STEM teaching intention. Additionally, gender did not affect preservice teachers’ intention to adopt STEM teaching approaches. However, preservice teachers whose specialization was in different fields tended to influence their knowledge and perceived behavioral control; these issues require further investigation.

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.

Fig. 1
Fig. 2

Similar content being viewed by others

References

  • Accreditation Board for Engineering and Technology (2002). Engineering accreditation criteria. Baltimore, MD: Author.

  • Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. In J. Kuhl & J. Beckman (Eds.), Action-control: From cognition to behavior (pp. 11–39). Heidelberg, Germany: Springer.

    Chapter  Google Scholar 

  • Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.

    Article  Google Scholar 

  • Ajzen, I. & Madden, T. J. (1986). Prediction of goal directed behavior: Attitudes, intentions, and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453–474.

    Article  Google Scholar 

  • American Association for the Advancement of Science (1993). Benchmarks for science literacy: Project 2061. New York, NY: Oxford University Press.

    Google Scholar 

  • Bybee, R. W. (2010). What is STEM education? Science, 329, 996.

    Article  Google Scholar 

  • Bybee, R. W. (2013). The case for STEM education: Challenges and opportunities. Arlington, VA: National Science Teachers Association.

    Google Scholar 

  • Chen, X. (2009). Students who study science, technology, engineering, and mathematics (STEM) in postsecondary education. Washington, DC: National Center for Education Statistics.

    Google Scholar 

  • Chin, W. W. (1998). The partial least squares approach for structural equation modeling. In G. A. Marcoulides (Ed.), Modern methods for business research (pp. 295–336). Mahwah, NJ: Erlbaum.

  • Chin, W. W. (2010). Bootstrap cross-validation indices for PLS path model assessment. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds.), Handbook of partial least squares (pp. 83–97). Heidelberg, Germany: Springer.

    Chapter  Google Scholar 

  • Darling-Hammond, L. & McLaughlin, M. W. (1995). Policies that support professional development in an era of reform. Phi Delta Kappan, 76(8), 597–604.

    Google Scholar 

  • DeVellis, R. F. (2003). Scale development: Theory and applications (2nd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Gura, M. (2012). Lego robotics: STEM sport of the mind. Learning & Learning with Technology, 40(1), 12–16.

    Google Scholar 

  • Han, S., Capraro, R. & Capraro, M. M. (2014). How science, technology, engineering, and mathematics (STEM) project-based learning (PBL) affects high, middle, and low achievers differently: The impact of student factors on achievement. International Journal of Science and Mathematics Education. Advance online publication. doi:10.1007/s10763-014-9526-0

  • Huang, T. (2012). Agents’ social imagination: The ‘invisible’ hand of neoliberalism in Taiwan’s curriculum reform. International Journal of Educational Development, 32(1), 39–45.

    Article  Google Scholar 

  • International Technology Education Association (2007). Standards for technological literacy: Content for the study of technology (3rd ed.). Reston, VA: Author.

  • Kolter, P. (2000). Marketing management. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  • Kuenzi, J. J. (2008). Science, technology, engineering, and mathematics (STEM) education: Background, federal policy, and legislative action. Washington, DC: Congressional Research Service.

    Google Scholar 

  • Mahoney, M. (2010). Students’ attitudes toward STEM: Development of an instrument for high school STEM-based programs. Journal of Technology Studies, 36(1), 24–34.

    Article  Google Scholar 

  • Ministry of Education (2013). Yearbook of teacher education statistics: The Republic of China. Taipei, Taiwan: Author.

    Google Scholar 

  • Monroe, M., Day, B. & Grieser, M. (2000). GreenCOM weaves four strands. In B. Day & M. Monroe (Eds.), Environmental education & communication for a sustainable world (pp. 3–6). Washington, DC: Academy for Educational Development.

  • Nathan, M., Srisurichan, R., Walkington, C., Wolfgram, M., Williams, C. & Alibali, M. W. (2013). Building cohesion across representations: A mechanism for STEM integration. Journal of Engineering Education, 102(1), 209–223.

    Article  Google Scholar 

  • National Research Council (1996). The national science education standards. Washington, DC: National Academy Press.

    Google Scholar 

  • National Research Council (2012). Disciplinary core ideas-Engineering, Technology, and applications of Science. In H. Quinn & H. A. Schweingruber (Eds.), A framework for K-12 science education: Practices, crosscutting concepts, and core ideas (pp. 201–214). Washington, DC: National Academies Press.

  • Nicholls, G. M., Wolfe, H., Besterfield-Sacre, M. & Shuman, L. J. (2010). Predicting STEM degree outcomes based on eighth grade data and standard test scores. Journal of Engineering Education, 99(3), 209–223.

  • Nicholls, G. M., Wolfe, H., Besterfield-Sacre, M., Shuman, L. J. & Larpkiattaworn, S. (2007). A method for identifying variables for predicting STEM enrollment. Journal of Engineering Education, 96(1), 33–44.

  • Price, J. (2010). The effect of instructor race and gender on student persistence in STEM fields. Economics of Education Review, 29(6), 901–910.

    Article  Google Scholar 

  • Roberts, A. & Cantu, D. (2012). Applying STEM instructional strategies to design and technology curriculum. Proceedings of the Technology Education in the 21st Century, Sweden, 73, 111–118. Retrieved from http://www.ep.liu.se/ecp_article/index.en.aspx?issue=073;article=013

  • Sanders, M. (2012). Integrative STEM education as “best practice.” Paper presented at the Seventh Biennial International Technology Education Research Conference, Queensland, Australia. Retrieved from http://www.sp2.upenn.edu/ostrc/stem/ documents/IntegrativeSTEM.pdf

  • Tenenhaus, M. (2008). Component-based structural equation modelling. Total Quality Management & Business Excellence, 19, 871–886.

    Article  Google Scholar 

  • Vescio, V., Ross, D. & Adams, A. (2008). A review of research on the impact of professional learning communities on teaching practice and student learning. Teaching and Teacher Education, 24(1), 80–91.

  • Vinzi, V. E., Trinchera, L. & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In V. Esposito Vinzi, W. W. Chin, J. Henseler & H. Wang (Eds.), Handbook of partial least squares (pp. 47–82). Heidelberg, Germany: Springer.

Download references

Acknowledgements

This research was funded by the Ministry of Science and Technology of the Republic of China under contract numbers NSC 102-2628-S-003 -001 and MOST 103-2628-S-003 -001. The findings and recommendations contained in this article of those of the authors and do not necessarily reflect those of the Ministry of Science and Technology. We are extremely grateful to Professor Larry D. Yore and Shari A. Yore for their mentoring efforts, the reviewers for their helpful comments, and the preservice teachers who participated in this study.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Kuen-Yi Lin.

Appendix: The Preservice Teachers’ Integrative STEM Teaching Intention Questionnaire (7-point Likert Scale)

Appendix: The Preservice Teachers’ Integrative STEM Teaching Intention Questionnaire (7-point Likert Scale)

Knowledge Items

  1. 1.

    I am familiar with the Science knowledge in the middle school level (e.g. Newton’s laws of motion).

  2. 2.

    I am familiar with the Technology knowledge in the middle school level (e.g. technological problem-solving process, material processing, tool using).

  3. 3.

    I am familiar with the Engineering knowledge in the middle school level (e.g. engineering design, mechanical structure).

  4. 4.

    I am familiar with the Mathematical knowledge in the middle school level (e.g. measure, calculation, analysis).

Value Items

  1. 1.

    I think it is important to help students in learning how to collect STEM-related data during the learning process.

  2. 2.

    I think it is important to help students in learning how to use STEM-related data during the design process.

  3. 3.

    I think it is important to help students in learning how to use STEM-related data during the test and modify process.

  4. 4.

    I think it is helpful to improve students’ learning performance by guiding them in integrating STEM-related issues during the learning process.

  5. 5.

    I like to implement integrative STEM teaching activity.

  6. 6.

    I think it is helpful to teaching by caring for the STEM-related activities and news.

Attitude Items

  1. 1.

    I will implement integrative STEM teaching if media advertisements (e.g. newspaper, television) ask me to do this.

  2. 2.

    I will implement integrative STEM teaching if the school environment asks me to do this.

  3. 3.

    I will implement integrative STEM teaching if my university professors ask me to do this.

  4. 4.

    I will implement integrative STEM teaching if my colleagues ask me to do this.

  5. 5.

    I will implement integrative STEM teaching if my educational ideas ask me to do this.

  6. 6.

    I will implement integrative STEM teaching if my students ask me to do this.

Subjective Norm Items

  1. 1.

    In the teaching environment, I think I have enough ability in implementing integrative STEM teaching.

  2. 2.

    I know how to improve students’ learning performance through integrative STEM teaching.

  3. 3.

    I think it is easy for me to use my own STEM knowledge in implementing integrative STEM teaching.

  4. 4.

    I think I know how to propose STEM-based suggestions to students during the design process.

  5. 5.

    I think I know how to propose STEM-based suggestions to students during the test and modify process.

Perceived Behavioral Control Items

  1. 1.

    I will try my best to implement integrative STEM teaching in the future no matter what the future teaching environment is.

  2. 2.

    I will try to teach students in thinking how to propose their ideas according to their STEM knowledge during the design process.

  3. 3.

    I will try to teach students in thinking how to modify their product according to their STEM knowledge during the test and modify process.

  4. 4.

    I will try to remind students in solving problems according to their STEM knowledge instead of intuition.

  5. 5.

    I will try to collaborate with other teachers in STEM fields for implementing integrative STEM teaching.

Behavioral Intention Items

  1. 1.

    The integrative STEM teaching is helpful in developing students’ ability in integrating theory and practice.

  2. 2.

    Students can have better performance in hands-on learning activity if they can integrate their STEM knowledge in the process of design and making.

  3. 3.

    Students can solve problems appropriately in their daily life if they can integrate their STEM knowledge in the process of problem solving.

  4. 4.

    Students can explore their interest in STEM fields through integrative STEM teaching.

  5. 5.

    We can develop future talents in STEM fields through integrative STEM teaching.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lin, KY., Williams, P.J. Taiwanese Preservice Teachers’ Science, Technology, Engineering, and Mathematics Teaching Intention. Int J of Sci and Math Educ 14, 1021–1036 (2016). https://doi.org/10.1007/s10763-015-9645-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10763-015-9645-2

Keywords

Navigation