Core competencies of K-12 computer science education from the perspectives of college faculties and K-12 teachers
Keywords:
K-12 computer science education, core competencies, computational thinking, problem-solving, mathAbstract
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.
Downloads
References
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: https://doi.org/10.1145/1929887.1929905
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. https://doi.org/10.1145/3015455 DOI: https://doi.org/10.1145/3015455
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. https://komenskypost.nl/wp-content/uploads/2017/01/jrc104188_computhinkreport.pdf
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. https://search.informit.org/doi/abs/10.3316/informit.767807290396583 DOI: https://doi.org/10.14221/ajte.2017v42n3.4
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative research in psychology, 3(2), 77-101. DOI: https://doi.org/10.1191/1478088706qp063oa
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. https://doi.org/10.1080/08993408.2018.1547564
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. https://doi.org/10.29333/ejmste/93483
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. https://doi.org/10.1080/08993408.2019.1696169
Code.org, CSTA, & ECEP Alliance. (2020). 2020 State of Computer Science Education: Illuminating Disparities. https://advocacy.code.org/stateofcs
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 https://www.csteachers.org/page/standards
Dehouck, R. (2016). The maturity of visual programming. http://www.craft.ai/blog/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). https://doi.org/10.1145/1734263.1734437 DOI: https://doi.org/10.1145/1734263.1734437
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. https://doi.org/10.1016/j.compedu.2012.11.016 DOI: https://doi.org/10.1016/j.compedu.2012.11.016
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. https://doi.org/10.1145/2602483 DOI: https://doi.org/10.1145/2602483
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: https://doi.org/10.1037/10595-013
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. https://doi.org/10.1007/s11528-016-0098-4 DOI: https://doi.org/10.1007/s11528-016-0098-4
Grover, S. & Pea, R. (2013). Computational thinking in K–12: A review of the state of the field. Educational Researcher, 42 (1), 38–43. https://doi.org/10.3102/0013189X12463051 DOI: https://doi.org/10.3102/0013189X12463051
Guest, G. (2012). Applied thematic analysis. Sage. DOI: https://doi.org/10.4135/9781483384436
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. https://doi.org/10.1016/j.compedu.2018.07.004
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: https://doi.org/10.1007/s10763-017-9836-0
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. https://doi.org/10.1016/j.compedu.2014.11.022 DOI: https://doi.org/10.1016/j.compedu.2014.11.022
K-12 Computer Science Framework Steering Committee. (2016). K-12 computer science framework. ACM. doi:https://doi.org/10.1145/3079760
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. https://doi.org/10.1145/1089733.1089734 DOI: https://doi.org/10.1145/1089733.1089734
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. https://doi.org/10.1016/j.chb.2017.01.005 DOI: https://doi.org/10.1016/j.chb.2017.01.005
Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. Sage. DOI: https://doi.org/10.1016/0147-1767(85)90062-8
Lindh, J., & Holgersson, T. (2007). Does lego training stimulate pupils’ ability to solve logical problems?. Computers & Education, 49(4), 1097-1111. https://doi.org/10.1016/j.compedu.2005.12.008 DOI: https://doi.org/10.1016/j.compedu.2005.12.008
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: https://doi.org/10.21585/ijcses.v2i1.26
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. https://doi.org/10.1016/j.chb.2014.09.012 DOI: https://doi.org/10.1016/j.chb.2014.09.012
Malan, D. J., & Leitner, H. H. (2007). Scratch for budding computer scientists. ACM Sigcse Bulletin, 39(1), 223-227. https://doi.org/10.1145/1227504.1227388 DOI: https://doi.org/10.1145/1227504.1227388
Ministry of Education. (2014). Computer science: A new curriculum in reform. http://cms.education.gov.il/NR/rdonlyres/0E091CFA-8E73-4C24-96A7-0A6D23E571EA/189697/resource_849760831.pdf
Organisation for Economic Co-operation and Development. (2018). The future of education and skills: Education 2030. OECD Education Working Papers 23. https://doi.org/10.1111/j.1440-1827.2012.02814.x DOI: https://doi.org/10.1111/j.1440-1827.2012.02814.x
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. https://doi.org/10.1016/j.compedu.2016.03.003 DOI: https://doi.org/10.1016/j.compedu.2016.03.003
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: https://doi.org/10.1109/MPRV.2016.23
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. https://dl.acm.org/doi/pdf/10.1145/2593249?casa_token=zOwW-U2zltcAAAAA:RR8hxGKWuykHfnSlZpB_7z4pMY1oFKSWIm9W8txVT-NE4KLKx4JlagcXvX1w0z84VvEIScrM3xln
Selby, C., & Woollard, J. (2013). Computational thinking: The developing definition. https://eprints.soton.ac.uk/356481/
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 DOI: https://doi.org/10.1016/j.edurev.2017.09.003
Michigan Department of Education (2020, May). State of Computer Science Education in Michigan. https://www.michigan.gov/documents/mde/State_of_Computer_Science_Education_in_Michigan_Report_709699_7.pdf
The Horizon Report. (2017). K–12 edition. https://www.nmc.org/nmchorizon-k12/
TIOBE index. (2021). https://www.tiobe.com/tiobe-index
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. https://doi.org/10.1177/0735633117743918 DOI: https://doi.org/10.1177/0735633117743918
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: https://doi.org/10.1007/s10639-015-9412-6
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: https://doi.org/10.1007/s10639-016-9493-x
Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33-35. DOI: https://doi.org/10.1145/1118178.1118215
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. https://doi.org/10.1080/10494820.2018.1534245
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. https://doi.org/10.1080/08993408.2019.1565233
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. https://www.acrwebsite.org/volumes/11931
Published
How to Cite
Issue
Section
Copyright (c) 2023 Meina Zhu, Cheng Wang
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).