Core competencies of K-12 computer science education from the perspectives of college faculties and K-12 teachers


  • Meina Zhu Wayne State University
  • Cheng Wang Wayne State University


K-12 computer science education, core competencies, computational thinking, problem-solving, math


Given the increasing needs of employees with computational skills, understanding the core competencies of K-12 computer science (CS) education is vital. This phenomenological research aims to identify critical factors of CS education in K-12 schools from the perspectives and visions of CS faculties in higher education and teachers in K-12 schools. This study adopted a phenomenological research design. The researchers conducted a semi-structured interview with 13 CS faculties and K-12 CS teachers in Michigan and analyzed the data using thematic analysis. The findings indicated that: (1) the core competencies for K-12 CS education include problem-solving through computational thinking, math background, and foundational programming skills, and (2) what is essential is not the programming languages taught in K-12 schools but computational thinking, which enables the learners to easily transfer from one language environment to another. The findings provide important implications for K-12 CS education regarding the core competencies and programming languages to be taught.


Download data is not yet available.


Barr, V., & Stephenson, C. (2011). Bringing computational thinking to K-12: What is involved and what is the role of the computer science education community? ACM Inroads, 2(1), 48–54. Doi: 10.1145/1929887.1929905 DOI:

Bau, D., Gray, J., Kelleher, C., Sheldon, J., & Turbak, F. (2017, June). Learnable programming: Blocks and beyond. In the Communications of the ACM, 60(6), 72–80. DOI:

Bernard, H. R., & Ryan, G. W. (2009). Analyzing qualitative data: Systematic approaches. SAGE publications.

Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., Engelhardt, K., Kampylis, P., & Punie, Y. (2016). Developing computational thinking in compulsory education. European Commission, JRC Science for Policy Report, 68.

Bower, M., Wood, L. N., Lai, J. W., Highfield, K., Veal, J., Howe, C., ... & Mason, R. (2017). Improving the computational thinking pedagogical capabilities of school teachers. Australian Journal of Teacher Education (Online), 42(3), 53-72. DOI:

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. DOI:

Chen, C., Haduong, P., Brennan, K., Sonnert, G., & Sadler, P. (2019). The effects of first programming language on college students’ computing attitude and achievement: a comparison of graphical and textual languages. Computer Science Education, 29(1), 23-48.

Chou, P.-N. (2018). Skill development and knowledge acquisition cultivated by maker education: Evidence from Arduino-based educational robotics. EURASIA Journal of Mathematics, Science and Technology Education, 14(10), 1–15.

Çiftci, S., & Bildiren, A. (2020). The effect of coding courses on the cognitive abilities and problem-solving skills of preschool children. Computer Science Education, 30(1), 3-21., CSTA, & ECEP Alliance. (2020). 2020 State of Computer Science Education: Illuminating Disparities.

Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.

CSTA (n.d.). Computer science standards. CSTA. Retrieved from

Dehouck, R. (2016). The maturity of visual programming.

Enbody, R. J., & Punch, W. F. (2010, March). Performance of Python CS1 students in mid-level non-Python CS courses. In Proceedings of the 41st ACM technical symposium on Computer science education (pp. 520-523). DOI:

Erlandson, D. A., Harris, E. L., Skipper, B. L., & Allen, S. D. (1993). Doing naturalistic inquiry: A guide to methods. Sage.

Fessakis, G., Gouli, E., & Mavroudi, E. (2013). Problem solving by 5–6 years old kindergarten children in a computer programming environment: A case study. Computers & Education, 63, 87-97. DOI:

Gal-Ezer, J., & Stephenson, C. (2014). A tale of two countries: Successes and challenges in K-12 computer science education in Israel and the United States. ACM Transactions on Computing Education (TOCE), 14(2), 1-18. DOI:

Giorgi, A. P., & Giorgi, B. M. (2003). The descriptive phenomenological psychological method. In P. M. Camic, J. E. Rhodes, & L. Yardley (Eds.), Qualitative research in psychology: Expanding perspectives in methodology and design (pp. 243–273). American Psychological Association DOI:

Gretter, S., & Yadav, A. (2016). Computational thinking and media and information literacy: An integrated approach to teaching twenty-first century skills. TechTrends, 60(5), 510–516. DOI:

Grover, S. & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42 (1), 38–43. DOI:

Guest, G. (2012). Applied thematic analysis. Sage. DOI:

Hsu, T. C., Chang, S. C., & Hung, Y. T. (2018). How to learn and how to teach computational thinking: Suggestions based on a review of the literature. Computers & Education, 126, 296-310.

Irish, T., & Kang, N. H. (2018). Connecting classroom science with everyday life: Teachers’ attempts and students’ insights. International Journal of Science and Mathematics Education, 16(7), 1227-1245. Doi: 10.1007/s10763-017-9836-0 DOI:

Israel, M., Pearson, J. N., Tapia, T., Wherfel, Q. M., & Reese, G. (2015). Supporting all learners in school-wide computational thinking: A cross-case qualitative analysis. Computers & Education, 82, 263-279. DOI:

K-12 Computer Science Framework Steering Committee. (2016). K-12 computer science framework. ACM. doi:

Kelleher, C., & Pausch, R. (2005). Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, 37(2), 83–137. DOI:

Korkmaz, Ö., Çakir, R., & Özden, M. Y. (2017). A validity and reliability study of the computational thinking scales (CTS). Computers in human behavior, 72, 558-569. DOI:

Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage. DOI:

Lindh, J., & Holgersson, T. (2007). Does lego training stimulate pupils’ ability to solve logical problems?. Computers & Education, 49(4), 1097-1111. DOI:

Lockwood, J., & Mooney, A. (2018). Computational thinking in education: Where does it fit? A systematic literary review. International Journal of Computer Sciences and Engineering Systems, 2(1), 41–60. DOI:

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. DOI:

Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. ACM Sigcse Bulletin, 39(1), 223-227. DOI:

Ministry of Education. (2014). Computer science: A new curriculum in reform.

Organisation for Economic Co-operation and Development. (2018). The future of education and skills: Education 2030. OECD Education Working Papers 23. DOI:

Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. Basic Books, Inc.

Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice: The definitive text of qualitative inquiry frameworks and options (4th ed.). Thousand Oaks, California: SAGE Publications, Inc.

Saez-Lopez, J., Roman-Gonzalez, M., & Vazquez-Cano, E. (2016). Visual programming languages integrated across the curriculum in elementary school: A two-year case study using Scratch in five schools. Computers & Education, 97, 129–141. DOI:

Schmidt, A. (2016). Increasing Computer Literacy with the BBC micro: bit. IEEE Pervasive Computing, 15(2), 5-7. Doi: 10.1109/MPRV.2016.23 DOI:

Seehorn, D., Pirmann, T., Batista, L., Ryder, D., Sedgwick, V., O’Grady-Cunniff, D., Twarek, B., Moix, D., Bell, J., Blankenship, L., Pollock, L., & Uche, C. (2016). CSTA K-12 Computer Science standards 2016 revised. ACM Press.

Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition.

Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. DOI:

Michigan Department of Education (2020, May). State of Computer Science Education in Michigan.

The Horizon Report. (2017). K–12 edition.

TIOBE index. (2021).

Tran, Y. (2019). Computational thinking equity in elementary classrooms: What third-grade students know and can do. Journal of Educational Computing Research, 57(1), 3-31. DOI:

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. Doi: 10.1007/s10639-015-9412-6 DOI:

Webb, M., Davis, N., Bell, T., Katz, Y. J., Reynolds, N., Chambers, D. P., & Sysło, M. M. (2017). Computer science in K-12 school curricula of the 2lst century: Why, what and when?. Education and Information Technologies, 22(2), 445-468. Doi: 10.1007/s10639-016-9493-x DOI:

Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. DOI:

Wong, G. K. W., & Cheung, H. Y. (2020). Exploring children’s perceptions of developing twenty-first century skills through computational thinking and programming. Interactive Learning Environments, 28(4), 438-450.

World Bank. (2019). Children learning to code: Essential for 21st century human capital.

World Economic Forum. (2015). New vision for education unlocking the potential of technology.

Xu, Z., Ritzhaupt, A. D., Tian, F., & Umapathy, K. (2019). Block-based versus text-based programming environments on novice student learning outcomes: A meta-analysis study. Computer Science Education, 29(2-3), 177-204.

Yu, P., & Hai, T. (2005). A focus conversation model in consumer research: The incorporation of group facilitation paradigm in in-depth interviews. Asia Pacific Advances in Consumer Research, 6, 337–344.



How to Cite

Zhu, M., & Wang, C. (2023). Core competencies of K-12 computer science education from the perspectives of college faculties and K-12 teachers . International Journal of Computer Science Education in Schools, 6(2).