You are here:

Editorial - Developing Computationally Literate Teachers: Current Perspectives and Future Directions for Teacher Preparation in Computing Education

, University of Delaware, United States ; , Michigan State University, United States ; , Indiana University, 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


Since Wing (2006) advanced computational thinking (CT) as a way of introducing computer science (CS) ideas to all students, CS and CT education have experienced a resurgence across K-12 settings. This renewed interest is amplified by a number of national curriculum and reform efforts. Specifically, new sets of standards emphasizing CT have been developed and are included in the Next Generation Science Standards (NGSS Lead States, 2013), the International Society for Technology in Education Standards (ISTE) for students (ISTE, 2016), and the Computer Science Teachers Association Standards (CSTA, 2017). Further, a number of research-based curricula (e.g., Computer Science Principles), supported by federal agencies, have emerged that focus on big ideas in CS. Finally, policy initiatives such as CS for All, highlighted the importance of broadening participation in computing, particularly among females, under-represented minorities, and students with disabilities.


Mouza, C., Yadav, A. & Ottenbreit-Leftwich, A. (2018). Editorial - Developing Computationally Literate Teachers: Current Perspectives and Future Directions for Teacher Preparation in Computing Education. Journal of Technology and Teacher Education, 26(3), 333-352. Waynesville, NC USA: Society for Information Technology & Teacher Education. Retrieved January 22, 2019 from .

View References & Citations Map


  1. Bureau of Labor Statistics. (2018). Computer and information technology occupations: Occupational outlook handbook. Retrieved from
  2. Century, J., Lach, M., King, H., Rand, S., Heppner, C., Franke, B., & Westrick, J. (2013). Building an operating system for computer science. Chicago, IL: CEMSE, University of Chicago with UEI. Retrieved from
  3. Code (2016). Evaluation Summary Report 2015-2016. Retrieved from (2018). Advocate for computer science education. Retrieved from
  4. CSTA (2017). K-12 standards. Https:// 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. New York, NY: CSforAll.
  5. DiSessa, A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press. Mouza, Yadav, and Ottenbreit-Leftwich ing computer science program. ACM Inroads, 3(2), 47-53.
  6. Fayer, S., Lacey, A., & Watson, A. (2017). STEM occupations: Past, present, and future. Https:// DASHDASH
  7. Google LLC., & Gallup Inc. (2016). Diversity gaps in computer science: Exploring the underrepresentation of girls, Blacks and Hispanics. Retrieved October 1, 2017 from Grover, S., & Pea, R. (2013). Computational thinking in K–12 a review of the state of the field. Educational Researcher, 42 (1), 38–43.
  8. Guzdial, M. (2017, April). Phone interview with Paulo Blikstein.
  9. 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, 14(13), 1–13, 29.
  10. Lang, K., Galanos, R., Goode, J., Seehorn, D., Trees, F., Phillips, P. & Stephenson, C. (2013). Bugs in the system: Computer science teacher certification in the U.S. NY: Computer Science Teachers Association.
  11. Margolis, J., Goode, J., & Chapman, G. (2014). That classroom ‘magic’. Communications of the ACM, 57(7), 31-33.
  12. Menekse, M. (2015). Computer science teacher professional development in the United States: a review of studies published between 2004 and 2014, Computer Science Education, 25(4), 325-350,
  13. Mishra, P., & Koehler, M. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017-1054. Https://,R.,Uche,C.,Lake,P.,Baldwin,L.,Rosato, J., & Takkunen, C. (2016). A MOOC-based professional development model for CS principles. Available at:
  14. Mouza, C., Basu, S., Yang, H., & Pan, Y. (2018). New content for new times: Pre-service teachers’ exploration of computer programming in educational technology coursework. Society for Information Technology& Teacher Education, March 26-30, Washington, DC. Editorial: Developing Computationally Literate Teachers (2016). Implementation and outcomes on a three-pronged approach to professional development for computer science principles. Proceedings of Special Interest Group in Computer Science Education, March 2-5, Memphis, TN.
  15. Mouza, C., Yang, H., Pan, Y., Yilmaz Ozden, S., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to TPACK development. Australasian Journal of Educational Technology, 33(3), 61-76.
  16. NGSS Lead States. (2013). Next generation science standards: For states, by states. Washington, DC: National Academies Press. Patel, P. (October 2, 2017). More teachers, fewer 3D printers: How to improve K–12 computer science education. IEEE Spectrum. Https:// tech-talk /at-work /education /what-500-m ill ion-could-mean-for-k-12-computer-science-education
  17. Pollock, L., Mouza, C., Czik, A., Little, A., Coffey, D., & Buttram, J. (2017). From professional development to the classroom: Findings from CS K-12 teachers. Proceedings of Special Interest Group in Computer Science Education, March 8-11, Seattle, WA.
  18. Ravitz, J., Stephenson, C., Parker, K., & Blazevski, J. (2017). Lessons from evaluation of computer science teacher professional development in Google’s CS4HS program. ACM Transactions on Computing Education. 17(4), 1-16.
  19. Sadik, O., Leftwich, A.O., & Nadiruzzaman, H. (2017). Computational thinking conceptions and misconceptions: Progression of preservice teacher thinking during computer science lesson planning. In P. Rich& C. Hodges (Eds), Emerging research, practice, and policy on computational thinking (pp. 221-238). Springer, Cham.
  20. Saeli, M., Perrenet, J., Jochems, W.M.G., Zwaneveld, B., Nederland, O.U., & Centrum, R.D.M. (2011). Teaching programming in secondary school: A pedagogical content knowledge perspective. Informatics in Education, 10(1), 73–88. Http://,S.(2017).(2017,June).Phoneinterview with Paulo Blikstein. Shah, N., Lewis, C.M., Caires, R., Khan, N., Qureshi, A., Ehsanipour, D., &
  21. Gupta, N. (2013). Building equitable computer science classrooms: elements of a teaching approach. In Proceedings of the 44th ACM technical symposium on computer science education, March 6–9, Denver, CO, USA (pp. 263–268). New York, NY: ACM.
  22. Van Driel, J.H., Verloop, N., & De Vos, W. (1998). Developing science teachers’ pedagogical content knowledge. Journal of Research in Science Teaching, 35(6), 673-695.
  23. 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://,J.M.(2008).Computationalthinking and thinking about computing. Retrieved from
  24. Wing, J.M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. ., & Gretter, S. (2013). Professional development for CS teachers: A framework and its implementation. Retrieved from
  25. Yadav, A., Hong, H., & Stephenson, C. (2016). Computational thinking for all: Pedagogical approaches to embedding a 21st century problem solving in K-12 classrooms. TechTrends 60, 565-568. DOI:10.1007/s11528-0160087-7
  26. Yadav, A., Berges, M., Sands, P., & Good, J. (2016). Measuring computer science pedagogical content knowledge: An exploratory analysis of teaching vignettes to measure teacher knowledge. In ACM International Conference Proceeding Series(Vol. 13–15–Octo).
  27. 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, 14(1), Article 5. Https://
  28. Yadav, A., & Korb, J.T. (2012). Learning to teach computer science: The need for a methods course. Communications of the Association for Computing Machinery, 55(11), 31-33 Editorial: Developing Computationally Literate Teachers edge trajectories of computational thinking through a redesigned educational technology course. Proceedings of the International Conference of the Learning Sciences, June 23-27, London, UK.
  29. Zinth, J. (2016). Computer science in high school graduation requirements. ECS Education Trends (Updated). Education Commission of the States. Retrieved from:

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