Skip to main content
Log in

Measuring in-service teacher self-efficacy for teaching computational thinking: development and validation of the T-STEM CT

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Despite a growing recognition that K-12 teachers should be prepared to teach students computational thinking (CT) skills across disciplines, there is a lack of valid instrumentation that measures teachers’ efficacy beliefs to do so. This study addresses this problem by developing and validating an instrument that measures in-service teachers’ self-efficacy beliefs for teaching CT. In parallel, we conducted a regression analysis to predict teachers’ self-efficacy and outcome expectancy beliefs for teaching CT based on demographic traits of the respondents. We surveyed a total of 330 K-12 in-service teachers. A combination of classical test theory and item response theory Rasch was used to validate the instrument. Our results yielded a valid and reliable tool measuring teaching efficacy beliefs for CT. Based on the differential item functioning analysis, the instrument did not reflect bias with gender, race, or teaching experience. Additionally, a regression analysis did not reveal significant predictors using teachers’ demographic characteristics. This suggests a need for looking at other factors that may significantly predict K-12 teachers’ teaching efficacy beliefs for CT to inform theory and practice around successful CT teaching and learning. Furthermore, we provide implications for the instrument we have developed.

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

Data Availability

Not applicable.

Code availability

Not applicable.

References

  • Adams, R., & Wu, M. (2010). Notes and Tutorial ConQuest: Multidimensional Model. Retrieved from https://www.acer.org/conquest/notes-tutorials

  • Aho, A. V. (2012). Computation and computational thinking. The Computer Journal, 55(7), 832–835

    Article  Google Scholar 

  • American Educational Research Association, American Psychological Association, & National Council on Measurement in Education. (2014). Standards for educational and psychological testing. Washington, DC: AERA

    Google Scholar 

  • Angeli, C., Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 computational thinking curriculum framework: Implications for teacher knowledge. Journal of Educational Technology & Society, 19(3), 47–57

    Google Scholar 

  • Arbuckle, J. L. (2019). Amos (Version 26.0) [Computer software]. Chicago: SPSS

  • Bandura, A. (1977). Self-efficacy: Towards a unifying theory of behavioral change. Psychological Review, 84(2), 191–215

    Article  Google Scholar 

  • Bandura, A. (1986). Social foundations of thought and action: A social cognitive theory. Englewood Cliffs: Prentice-Hall

    Google Scholar 

  • Bandura, A. (1997). Self-efficacy: The exercise of control. New York: W. H. Freeman and Company

    Google Scholar 

  • Bandura, A., & Schunk, D. H. (1981). Cultivating competence, self-efficacy, and intrinsic interest through proximal self-motivation. Journal of Personality and Social Psychology, 41(3), 586–598

    Article  Google Scholar 

  • Bean, N., Weese, J., Feldhausen, R., & Bell, R. S. (2015). Starting from scratch: Developing a pre-service teacher training program in computational thinking. In 2015 IEEE Frontiers in Education Conference (FIE) (pp. 1–8). IEEE

  • Bond, T. G., & Fox, C. M. (2001). Applying the Rasch model: Fundamental measurement in the human sciences. Mahwah: Lawrence Erlbaum Assoc

    Book  Google Scholar 

  • Boone, W. J., Staver, J. R., & Yale, M. S. (2014). Rasch analysis in the human sciences. Dordrecht: Springer

    Book  Google Scholar 

  • Bower, M., Wood, L. N., Lai, J. W., Howe, C., Lister, R., Mason, R., ... & Veal, J. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education, 42(3). https://doi.org/10.14221/ajte.2017v42n3.4.

  • Cakiroglu, J., Capa-Aydin, Y., & Woolfolk Hoy, A. W. (2012). Science teaching efficacy beliefs. In B. Fraser, K. Tobin, & C. McRobbie (Eds.), Second international handbook of science education (pp. 449–461). Netherlands: Springer

    Chapter  Google Scholar 

  • Cetin, I. (2016). Preservice teachers’ introduction to computing: exploring utilization of scratch. Journal of Educational Computing Research, 54(7), 997–1021. https://doi.org/10.1177/0735633116642774.

    Article  Google Scholar 

  • Code.org Advocacy Coalition. (2018). 2018 state of computer science education. Retrieved from https://code.org/files/2018_state_of_cs.pdf

  • Davis, S., Ravitz, J., & Blazevski, J. (2018). Evaluating computer science professional development models and educator outcomes to ensure equity. In 2018 Research on Equity and Sustained Participation in Engineering, Computing, and Technology (RESPECT) (pp. 1–4). IEEE

  • DeVellis, R. F. (2016). Scale development: Theory and applications (4th). Los Angeles: Sage

    Google Scholar 

  • Eccles, J. S., & Wigfield, A. (2002). Motivational beliefs, values, and goals. Annual Review of Psychology, 53, 109–132. https://doi.org/10.1146/annurev.psych.53.100901.1

    Article  Google Scholar 

  • Enochs, L. G., & Riggs, I. M. (1990). Further development of an elementary science teaching efficacy belief instrument: A preservice elementary scale. School Science and Mathematics, 90(8), 694–706

    Article  Google Scholar 

  • Enochs, L. G., Smith, P. L., & Huinker, D. (2000). Establishing factorial validity of the mathematics teaching efficacy beliefs instrument. School Science and Mathematics, 100(4), 194–202

    Article  Google Scholar 

  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research in Teacher Education, 42(3), 255–284. https://doi.org/10.1080/15391523.2010.10782551.

    Article  Google Scholar 

  • Ertmer, P. A., Ottenbreit-Leftwich, A. T., Sadik, O., Sendurur, E., & Sendurur, P. (2012). Teacher beliefs and technology integration practices: A critical relationship. Computers & Education, 59(2), 423–435. https://doi.org/10.1016/j.compedu.2012.02.001.

    Article  Google Scholar 

  • Faul, F., Erdfelder, E., Buchner, A., & Lang, A.-G. (2009). Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses. Behavior Research Methods, 41, 1149–1160

    Article  Google Scholar 

  • Garvin, M., Killen, H., Plane, J., & Weintrop, D. (2019). Primary school teachers’ conceptions of computational thinking. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 899–905). ACM

  • Gehlbach, H., & Brinkworth, M. E. (2011). Measure twice, cut down error: A process for enhancing the validity of survey scales. Review of General Psychology, 15(4), 380–387. https://doi.org/10.1037/a0025704.

    Article  Google Scholar 

  • George, D., & Mallery, M. (2016). IBM SPSS statistics 23 step by step: A simple guide and reference (14th). New York: Routledge

    Book  Google Scholar 

  • González-Pérez, M., Susi, R., Barrio, A., & Antona, B. (2018). Five levels of performance and two subscales identified in the computer-vision symptom scale (CVSS17) by Rasch, factor, and discriminant analysis. PLoS One 13(8). https://doi.org/10.1371/journal.pone.0202173.

  • Grover, S., & Pea, R. (2013). Computational thinking in K-12: A review of the state of the field. Educational Researcher, 42(1), 38–42. https://doi.org/10.3102/0013189X12463051.

    Article  Google Scholar 

  • Grover, S., & Pea, R. (2018). Computational Thinking: A competency whose time has come. In S. Sentance, E. Barendsen, & C. Shulte (Eds.), Computer science education: Perspectives on teaching and learning in school (pp. 19–37). London: Bloomsbury Academic

    Google Scholar 

  • Guzdial, M. (2011). A definition of computational thinking from Jeannette Wing. Computing Education Research Blog. http://computinged.wordpress.com/2011/03/22/a-definition-of-computational-thinking-from-jeanette-wing/

  • Guzdial, M., Ericson, B., Mcklin, T., & Engelman, S. (2014). Georgia computes! An intervention in a US state, with formal and informal education in a policy context. ACM Transactions on Computing Education (TOCE), 14(2), 13

    Google Scholar 

  • Hair, J. F., Black, B., Babin, B. J., & Anderson, W. C. (2010). Multivariate data analysis: A global perspective (7th ed.). Pearson.

  • Hammack, R., & Ivey, T. (2017). Examining elementary teachers’ engineering self-efficacy and engineering teacher efficacy. School Science and Mathematics, 117(1–2), 52–62

    Article  Google Scholar 

  • Hardesty, D. M., & Bearden, W. O. (2004). The use of expert judges in scale development: Implications for improving face validity of measures of unobservable constructs. Journal of Business Research 57(2), 98–107. https://doi.org/10.1016/S0148-2963(01)00295-8.

    Article  Google Scholar 

  • Haseski, H. I., & Ílíc, U. (2019). An investigation of the data collection instruments developed to measure computational thinking. Informatics in Education, 18(2), 297–319. https://doi.org/10.15388/infedu.2019.14.

    Article  Google Scholar 

  • Henderson, P. B. (2009). Ubiquitous computational thinking. Computer, 42(10), 100–102

    Article  Google Scholar 

  • Huck, S. W. (2000). Reading statistics and research (3rd). New York: Longman

    Google Scholar 

  • Hu, C. (2011). Computational thinking: what it might mean and what we might do about it. In Proceedings of the 16th annual joint conference on Innovation and technology in computer science education. ACM

  • International Society for Technology in Education. (2018). ISTE announces new computational thinking standards for all educators. [Web blog post]. Retrieved from https://www.iste.org/explore/Press-Releases/ISTE-Announces-New-Computational-Thinking-Standards-for-All-Educators

  • International Society for Technology in Education. (2019). Computational Thinking Competencies. Retrieved from https://www.iste.org/standards/computational-thinking

  • K–12 Computer Science Framework. (2016). Retrieved from http://www.k12cs.org

  • Karabenick, S. A., Woolley, M. E., Friedel, J. M., Ammon, B. V., Blazevski, J., Bonney, C. R., … Kelly, K. L. (2007). Cognitive processing of self-report items in educational research: Do they think what we mean?. Educational Psychologist 42(3): 139-151

  • Kaya, E., Newley, A., Yesilyurt, E., & Deniz, H. (2020). Measuring computational thinking teaching efficacy beliefs of preservice elementary teachers. Journal of College Science Teaching 49(6)

  • Klassen, R. M., & Chiu, M. M. (2010). Effects on teachers’ self-efficacy and job satisfaction: Teacher gender, years of experience, and job stress. Journal of Educational Psychology, 102(3), 741–756. https://doi.org/10.1037/a0019237.

    Article  Google Scholar 

  • Klassen, R. M., & Tze, V. M. (2014). Teachers’ self-efficacy, personality, and teaching effectiveness: A meta-analysis. Educational Research Review, 12, 59–76. https://doi.org/10.1016/j.edurev.2014.06.001.

    Article  Google Scholar 

  • Kong, S. C., & Lao, A. C. C. (2019). Assessing in-service teachers’ development of computational thinking practices in teacher development courses. In Proceedings of the 50th ACM Technical Symposium on Computer Science Education (pp. 976–982). ACM

  • 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), 1–21

    Article  Google Scholar 

  • Lekhu, M. A. (2013). Relationship between self-efficacy beliefs of science teachers and their confidence in content knowledge. Journal of Psychology in Africa, 23(1), 109–112. https://doi.org/10.1080/14330237.2013.10820602.

    Article  Google Scholar 

  • Linacre, J. M. (2012). A user's guide to winstep. Ministep Rasch-Model Computer Programs: Program Manual

  • Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014). Computational thinking in K-9 education. In Proceedings of the Working Group Reports of the 2014 on Innovation & Technology in Computer Science Education Conference (pp. 1–29)

  • Messick, S. (1995). Validity of psychological assessment: Validation of inferences from persons’ responses and performances as scientific inquiry into score meaning. American Psychologist, 50(9), 741–749

    Article  Google Scholar 

  • Mishra, P., Yadav, A., & Deep-Play Research Group. (2013). Rethinking technology & creativity in the 21st century. TechTrends, 57(3), 10–14

    Article  Google Scholar 

  • National Center for Educational Statistics. (2020). https://nces.ed.gov/programs/coe/indicator_clr.asp

  • National Research Council. (2010). Committee for the Workshops on Computational Thinking: Report of a workshop on the scope and nature of computational thinking. Washington, DC: National Academies Press. Retrieved from https://www.nap.edu/catalog/12840/report-of-a-workshop-on-the-scope-and-nature-of-computational-thinking

  • National Research Council. (2011). Committee for the Workshops on Computational Thinking: Report of a workshop of pedagogical aspects of computational thinking. Washington, DC: National Academies Press. Retrieved from https://www.nap.edu/catalog/13170/report-of-a-workshop-on-the-pedagogical-aspects-of-computational-thinking

  • Pajares, M. F. (1992). Teachers’ beliefs and educational research: Cleaning up a messy construct. Review of Educational Research, 62(3), 307–332

    Article  Google Scholar 

  • Petscher, Y., Schatschneider, C., & Compton, D. L. (2013). Applied quantitative analysis in education and the social sciences. New York: Routledge

    Book  Google Scholar 

  • Pichardo, M. C., Cano, F., Garzón-Umerenkova, A., Fuente de la, J., Peralta-Sánchez, F. J., Amate-Romera, J. (2018). Self-regulation questionnaire (SRQ) in Spanish adolescents: factor structure and rasch analysis. Frontiers in Psychology 9. https://doi.org/10.3389/fpsyg.2018.01370.

  • Rachmatullah, A., Akram, B., Boulden, D., Mott, B., Boyer, K., Lester, J., & Wiebe, E. (2020a). Development and validation of the middle grades computer science concept inventory (MG-CSCI) assessment. EURASIA Journal of Mathematics, Science and Technology Education, 16(5), 1–11. https://doi.org/10.29333/ejmste/116600

  • Rachmatullah, A., Alexander, A., & Wiebe, E. (2019). An alternative to STEBI-A: Validation of the T-STEM science scale. Paper presented at 2019 Conference of the European Science Education Research Association (ESERA’19). Bologna, Italy. August 26 – 30.

  • Rachmatullah, A., Wiebe, E., Boulden, D., Mott, B., Boyer, K., & Lester, J. (2020b). Development and validation of the computer science attitudes scale for middle school students (MG-CS Attitudes). Computers in Human Behavior Reports, 2, 1–10. https://doi.org/10.1016/j.chbr.2020.100018

  • Ravitz, J., Stephenson, C., Parker, K., & Blazevski, J. (2017). Early lessons from evaluation of computer science teacher professional development in Google’s CS4HS program. ACM Transactions on Computing Education (TOCE), 17(4), 21

    Google Scholar 

  • Reise, S. P., Widaman, K. F., & Pugh, R. H. (1993). Confirmatory factor analysis and item response theory: Two approaches for exploring measurement invariance. Psychological Bulletin, 114(3), 552–566. https://doi.org/10.1037/0033-2909.114.3.552.

    Article  Google Scholar 

  • Repenning, A., Lamprou, A., Petralito, S., & Basawapatna, A. (2019). Making Computer Science Education Mandatory: Exploring a Demographic Shift in Switzerland. In Proceedings of the 2019 ACM Conference on Innovation and Technology in Computer Science Education (pp. 422–428). ACM

  • Riggs, I. M., & Enochs, L. G. (1990). Toward the development of an elementary teacher’s science teaching efficacy belief instrument. Science Education, 74(6), 625–637

    Article  Google Scholar 

  • Riggs, I. M. (1988). The development of an elementary teachers’ science teaching efficacy belief instrument (Publication No. 8905728). [Doctoral dissertation, Kansas State University]. ProQuest Dissertations and Theses Global

  • Román-González, M., Pérez-González, J. C., & Jiménez-Fernández, C. (2017). Which cognitive abilities underlie computational thinking? Criterion validity of the Computational Thinking Test. Computers in Human Behavior, 72, 678–691

    Article  Google Scholar 

  • Sanford, J. F., & Naidu, J. T. (2016). Computational thinking concepts for grade school. Contemporary Issues in Education Research, 9(1), 23–32. https://doi.org/10.19030/cier.v9il.9547.

    Article  Google Scholar 

  • Schrum, L., & Sumerfield, S. (2019, November 20). Coding, robotics programs are engaging learners worldwide. [Web blog post]. Retrieved from https://www.iste.org/explore/Computer-Science/Coding%2C-robotics-programs-are-engaging-learners-worldwide

  • Selby, C. C., & Woollard, J. (2013). Computational thinking: The developing definition. Paper presented at the Special Interest Group on Computer Science Education (SIGCSE), Atlanta

  • Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/j.edurev.2017.09.003.

    Article  Google Scholar 

  • Swackhamer, L. E., Koellner, K., Basile, C., & Kimbrough, D. (2009). Increasing the self-efficacy of inservice teachers through content knowledge. Teacher Education Quarterly, 36(2), 63–78

    Google Scholar 

  • Tschannen-Moran, M., & Johnson, D. (2011). Exploring literacy teachers’ self-efficacy beliefs: Potential sources at play. Teaching and Teacher Education, 27(4), 751–761. https://doi.org/10.1016/j.tate.2010.12.005.

    Article  Google Scholar 

  • Tschannen-Moran, M., & McMaster, P. (2009). Sources of self-efficacy: Four professional development formats and their relationship to self-efficacy and implementation of a new teaching strategy. The Elementary School Journal, 110(2), 228–245

    Article  Google Scholar 

  • Tschannen-Moran, M., Woolfolk Hoy, A. W., & Hoy, W. K. (1998). Teacher efficacy: Its meaning and measure. Review of Educational Research, 68(2), 202–248

    Article  Google Scholar 

  • Unfried, A., Faber, M., Townsend, L., & Corn, J. (2014). Validated student, teacher, and principal survey instruments for STEM education programs [Paper presentation]. Denver, CO: American Evaluation Association 28thAnnual Conference

  • Usher, E. L., & Pajares, F. (2008). Sources of self-efficacy in school: Critical review of the literature and future directions. Review of Educational Research, 78(4), 751–796

    Article  Google Scholar 

  • Voogt, J., Fisser, P., Good, J., Mishra, P., & Yadav, A. (2015). Computational thinking in compulsory education: Towards an agenda for research and practice. Education and Information Technologies, 20(4), 715–728. https://doi.org/10.1007/s10639-015-9412-6.

    Article  Google Scholar 

  • Wheatley, K. F. (2005). The case for reconceptualizing teacher efficacy research. Teaching and Teacher Education, 21(7), 747–766. https://doi.org/10.1016/j.tate.2005.05.009.

    Article  Google Scholar 

  • Wing, J. (2006). Computational thinking. Communications of the ACM, 49(3), 33–36

    Article  Google Scholar 

  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725

    Article  MathSciNet  MATH  Google Scholar 

  • Wing, J. (2010, November). Research notebook: Computational thinking—What and why? The Link Magazine. Retrieved from https://www.cs.cmu.edu/~CompThink/resources/TheLinkWing.pdf

  • World Economic Forum. (2016). The future of jobs: Employment, skills, and workforce strategy for the fourth industrial revolution. World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs.pdf

  • World Economic Forum. (2018). The future of jobs report 2018. Geneva, Switzerland: World Economic Forum. Retrieved from http://www3.weforum.org/docs/WEF_Future_of_Jobs_2018.pdf

  • Wozney, L., Venkatesh, V., & Abrami, P. (2006). Implementing computer technologies: Teachers’ perceptions and practices. Journal of Technology and Teacher Education, 14(1), 173–207

    Google Scholar 

  • Wright, B. D., & Linacre, J. M. (1994). Reasonable mean square fit values. Rasch Measurement Transactions, 8(3), 370

    Google Scholar 

  • Yadav, A., & Berges, M. (2019). Computer science pedagogical content Knowledge: characterizing teacher performance. ACM Transactions on Computing Education (TOCE), 19(3), 29

    Google Scholar 

  • Yadav, A., Mayfield, C., Zhou, N., Hambrusch, S., & Korb, J. T. (2014). Computational thinking in elementary and secondary teacher education. ACM Transactions on Computing Education (TOCE), 14(1), 5. https://doi.org/10.1145/2576872.

    Article  Google Scholar 

  • Yadav, A., Zhou, N., Mayfield, C., Hambrusch, S., & Korb, J. T. (2011). Introducing computational thinking in education courses. In Proceedings of the 42nd ACM Technical Symposium on Computer Science Education (pp. 465–470). ACM

  • Yan, Z., Brubacher, S., Boud, D., & Powell, M. (2020). Psychometric properties of the self‐assessment practice scale for professional training contexts: evidence from confirmatory factor analysis and Rasch analysis. International Journal of Training and Development, 24(4), 357–373. https://doi.org/10.1111/ijtd.12201

  • Zee, M., Koomen, H. M., Jellesma, F. C., Geerlings, J., & de Jong, P. F. (2016). Inter-and intra-individual differences in teachers’ self-efficacy: A multilevel factor exploration. Journal of School Psychology, 55, 39–56. https://doi.org/10.1016/j.jsp.2015.12.003.

    Article  Google Scholar 

Download references

Acknowledgments

This material is based upon work supported by the National Science Foundation under Grant No. 1837439.

Funding

This material is based upon work supported by the National Science Foundation under Grant No. 1837439.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Danielle Cadieux Boulden.

Ethics declarations

Conflicts of interest/Competing interests

The authors received funding from the National Science Foundation under Grant No. 1837439.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Boulden, D.C., Rachmatullah, A., Oliver, K.M. et al. Measuring in-service teacher self-efficacy for teaching computational thinking: development and validation of the T-STEM CT. Educ Inf Technol 26, 4663–4689 (2021). https://doi.org/10.1007/s10639-021-10487-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-021-10487-2

Keywords

Navigation