You are here:

Pre-service Teachers’ Perceptions of Computational Thinking

, , University of Minnesota, United States

Journal of Technology and Teacher Education Volume 26, Number 3, ISSN 1059-7069 Publisher: Society for Information Technology & Teacher Education, Waynesville, NC USA


Computational thinking (CT) is a process and skill set that is being expected of our students after their K-12 education. The issue is that most teachers do not know how to build CT expertise within their students. To address this challenge and create a blueprint for how to prepare pre-service teachers for their role ahead, we conducted a case study with two separate course designs within an initial teacher licensure course on technology. The purpose of this study was to investigate how pre-service teachers perceive CT and how they conceptualize CT within K-12 education. Our findings include insights of how pre-service teachers make sense of CT, the intersection of CT with their educational beliefs, and the complicated nexus of technology integration, computer science, and CT. We discuss tangible strategies for teacher educators who are including CT into their course or workshops.


Chang, Y.h. & Peterson, L. (2018). Pre-service Teachers’ Perceptions of Computational Thinking. Journal of Technology and Teacher Education, 26(3), 353-374. Waynesville, NC USA: Society for Information Technology & Teacher Education. Retrieved January 17, 2019 from .

View References & Citations Map


  1. Ahamed, S.I., Brylow, D., Ge, R., Madiraju, P., Merrill, S.J., Struble, C.A., & Early, J.P. (2010, March). Computational thinking for the sciences: a three day workshop for high school science teachers. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 42-46).
  2. Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). In Kampylis, P., & Punie, Y. (Eds.), Developing computational thinking in compulsory education– Implications for policy and practice. Luxembourg: Publications Office of the European Union. Retrieved from
  3. Brennan, K., & Resnick, M. (2012, April). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (pp. 1-25).
  4. Burden, K., Aubusson, P., Brindley, S., & Schuck, S. (2016). Changing knowledge, changing technology: implications for teacher education futures. Journal of Education for Teaching, 42(1), 4-16.
  5. Buss, A., & Gamboa, R. (2017). Teacher transformations in developing computational thinking: Gaming and robotics use in after-school settings. In Emerging Research, Practice, and Policy on Computational Thinking (pp. 189-203). Cham, Switzerland: Springer.
  6. Delyser, L.A., Goode, J., Guzdial, M., Kafai, Y., & Yadav, A. (2018). Priming the computer science teacher pump: Integrating computer science education into schools of education [PDF file]. Retrieved from
  7. Denning, P.J. (2009). The profession of IT Beyond computational thinking. Communications of the ACM, 52(6), 28-30.
  8. Epstein, D., & Miller, R.T. (2011). Slow off the Mark: Elementary School Teachers and The Crisis in Science, Technology, Engineering, and Math Education [PDF file]. Retrieved from Ertmer, P.A., & Ottenbreit-Leftwich, A.T. (2010). Teacher technology change: How knowledge, confidence, beliefs, and culture intersect. Journal of Research on Technology in Education, 42(3), 255-284.
  9. Fishman, B.J., Davis, E.A., & Chan, C.K.K. (2014). A learning sciences perspective on teacher learning research. In Sawyer, R.K. (Ed.). The Cambridge Handbook of the Learning Sciences: 2nd Edition. P750-p769. Cambridge,
  10. Freeman, A., Adams Becker, S., Cummins, M., Davis, A., & Hall Giesinger, C. (2017). NMC/CoSN Horizon Report: 2017 K–12 Edition. Austin, Texas: The New Media Consortium.
  11. International Society for Technology in Education. (2016). ISTE Standards for Students [PDF file]. Retrieved from Jaipal-Jamani, K., & Angeli, C. (2017). Effect of Robotics on Elementary Preservice Teachers’ Self-Efficacy, Science Learning, and Computational
  12. Kleiner, B., Thomas, N., & Lewis, L. (2007). Educational Technology in Teacher Education Programs for Initial Licensure [PDF file]. Washington, DC: National Center for Education Statistics, Institute of Education Sciences& U.S. Department of Education. Retrieved from
  13. Lye, S.Y., & Koh, J.H.L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12?. Computers in Human Behavior, 41, 51-61.
  14. Mannila, L., Dagiene, V., Demo, B., Grgurina, N., Mirolo, C., Rolandsson, L., & Settle, A. (2014, June). 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). ACM.
  15. Merriam, S.B. (1988). Case study research in education: A qualitative approach. San Francisco, CA: Jossey-Bass. Chang and Peterson edge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054.
  16. Morreale, P., & Joiner, D. (2011). Changing perceptions of computer science and computational thinking among high school teachers. Journal of Computing Sciences in Colleges, 26(6), 71-77.
  17. Mouza, C., Karchmer-Klein, R., Nandakumar, R., Ozden, S.Y., & Hu, L. (2014). Investigating the impact of an integrated approach to the development of preservice teachers’ technological pedagogical content knowledge (TPACK). Computers& Education, 71, 206-221.
  18. Mouza, C., Yang, H., Pan, Y.C., Ozden, S.Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3).
  19. Papert, S. (1996). An exploration in the space of mathematics educations. International Journal of Computers for Mathematical Learning, 1(1), 95-123.
  20. Sadik, O., Leftwich, A.O., & Nadiruzzaman, H. (2017). Computational thinking conceptions and misconceptions: progression of pre-service teacher thinking during computer science lesson planning. In Emerging Research, Practice, and Policy on Computational Thinking (pp. 221-238). Cham, Switzerland:
  21. Wing, J.M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35.
  22. 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.
  23. Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60(6), 565-568.
  24. Yadav, A., Gretter, S., Good, J., & McLean, T. (2017). Computational thinking in teacher education. In Emerging Research, Practice, and Policy on Computational Thinking (pp. 205-220). Cham, Switzerland: Springer.
  25. Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 80(4), 55-62.

These references have been extracted automatically and may have some errors. If you see a mistake in the references above, please contact