Skip to main content

Advertisement

Log in

EdTech Leaders’ Beliefs: How are K-5 Teachers Supported with the Integration of Computer Science in K-5 Classrooms?

  • Original research
  • Published:
Technology, Knowledge and Learning Aims and scope Submit manuscript

Abstract

Educational Technology Leaders’ support of computer science teachers in K-5 classrooms are influenced by their beliefs about school-based program implementation criteria, available district-level support, and state mandates on the integration of computer science. The researcher in this study examines the beliefs about Computer Science teacher support, and training in five different Educational Tech Leaders’ districts, to determine sustainable implementation practices for K-5 schools. In order to effectively integrate computer science in K-5 instruction, administrators and program decision-makers must be aware of the beliefs Educational Technology Leaders hold related to the implementation process of programs, specifically related to the training of K-5 teachers who facilitate the computer science curricula in classrooms. Information reported in this study may inform school-level, district-level, and state-level decisions related to sustainable computer science program implementations.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  • Angeli, C., & Valanides, N. (2005). Preservice teachers as ICT designers: An instructional design model based on an expanded view of pedagogical content knowledge. Journal of Computer-Assisted Learning, 21(4), 292–302.

    Article  Google Scholar 

  • Angeli, C., & Valanides, N. (2009). Epistemological and methodological issues for the conceptualization, development, and assessment of ICT-TPCK: Advances in technological pedagogical content knowledge (TPCK). Computers & Education, 52, 154–168.

    Article  Google Scholar 

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

    Google Scholar 

  • 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.

    Article  Google Scholar 

  • Brown, N. C., Sentance, S., Crick, T., & Humphreys, S. (2014). Restart: the resurgence of computer science in UK schools. ACM Transactions on Computing Education (TOCE), 14, 2–9.

    Google Scholar 

  • Calao, L. A., Moreno-León, J., Correa, H. E., & Robles, G. (2015). Developing mathematical thinking with scratch: An experiment with 6th grade students. In Design for teaching and learning in a networked world, 10th European conference on technology enhanced learning, EC-TEL 2015 (pp. 17–27). Toledo, Spain: Springer.

  • California State Board of Education. (2017). Computer Science Education. Information and resources related to computer science education for students in grades K-12a. Retrieved from http://www.cde.ca.gov/be/st/ss/computerscicontentstds.asp.

  • Charmaz, K. (2014). Constructing grounded theory: A practical guide through qualitative analysis (2nd ed.). Thousands Oaks, CA: Sage.

    Google Scholar 

  • Code.org and diversity in computer Science. (2017). Retrieved from https://code.org/diversity.

  • Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). New York, NY: Routledge.

    Google Scholar 

  • Cooper, S., Bookey, L., & GruenBaum, P. (2014). Future directions in computing education summit part one: Important computing education research questions. Technical Report CS-TR-14-0108-SC. Stanford: Stanford InfoLab.

  • Creswell, J. W. (2013). Qualitative inquiry and research design choosing among five approaches (3rd ed.). Thousand Oaks, CA: Sage.

    Google Scholar 

  • Cuny, J. (2012). Transforming high school computing: A call to action. ACM Inroads, 3(2), 32–36.

    Article  Google Scholar 

  • de Raadt, M., Watson, R., & Toleman, M. (2004). Introductory programming: What’s happening today and will there be any students to teach tomorrow? Australian Computer Science Communications, 26, 277–284.

    Google Scholar 

  • Deng, F., Chai, C., So, H. J., Qian, Y., & Chen, L. (2017). Examining the validity of the technological pedagogical content knowledge (TPACK) framework for preservice chemistry teachers. Australasian Journal or Educational Technology, 33(3), 1–14.

    Google Scholar 

  • Ferster, B. (2014). Teaching machines: Learning from the intersection of education and technology. Washington, DC: JHU Press.

    Google Scholar 

  • Feurzeig, W. (2010). Toward a culture of creativity: A personal perspective on Logo’s early years and ongoing potential. International Journal of Computers for Mathematical Learning, 15(3), 257–265.

    Article  Google Scholar 

  • Florida Department of Education. (2017). Computer science body of knowledge. Retrieved from http://www.fldoe.org/.

  • Fluck, A., Webb, M., Cox, M., Angeli, C., Malyn-Smith, J., Voogt, J., et al. (2016). Arguing for computer science in the school curriculum. Journal of Educational Technology & Society, 19(3), 38–46.

    Google Scholar 

  • Gal-Ezer, J., & Stephenson, C. (2010). Computer science teacher preparation is critical. ACM Inroads, 1, 61–66.

    Article  Google Scholar 

  • Google. (2015). Searching for computer science: Access and barriers in U.S. K-12 education. Retrieved from https://services.google.com/fh/files/misc/searching-for-computer-science_report.pdf.

  • Groth, R., Spickler, D., Bergner, J., & Bardzell, M. (2009). A qualitative approach to assessing technological pedagogical content knowledge. Contemporary Issues in Technology and Teacher Education, 9(4). Retrieved from http://www.citejournal.org/volume-9/issue-4-09/mathematics/a-qualitative-approach-to-assessing-technological-pedagogical-content-knowledge.

  • Grover, S., & Pea, R. (2013). Computational thinking in K12: A review of the state of the field. Educational Researcher, 42(1), 38–43.

    Article  Google Scholar 

  • Howard, N. R., & Howard, K. E. (2017). Using tablet technologies to engage and motivate urban high school students. International Journal of Educational Technology, 4(2), 66–74.

    Google Scholar 

  • Hubwieser, P., Magenheim, J., Mühling, A., & Ruf, A. (2013). Towards a conceptualization of pedagogical content knowledge for computer science. In Proceedings of the ninth annual international ACM conference on International computing education research (pp. 1–8). New York, NY: ACM.

  • Jones, M. J., & Dexter, S. S. (2018). Teacher perspectives on technology integration professional development: Formal, informal, and independent learning activities. Journal of Educational Multimedia & Hypermedia, 27(1), 83–102.

    Google Scholar 

  • Knochel, A. A., & Patton, R. R. (2015). If art education then critical digital making: Computational thinking and creative code. Studies in Art Education, 57(1), 21–38.

    Article  Google Scholar 

  • Koehler, M. J., Mishra, P., Kereluik, K., Shin, T. S., & Graham, C. (2014). The technological pedagogical content knowledge framework. In M. Spector, M. D. Merrill, J. Elen, & M. J. Bishop (Eds.), Handbook of research on educational communications and technology (4th ed., pp. 101–111). New York, NY: Springer.

    Chapter  Google Scholar 

  • Legewie, J., & DiPrete, T. A. (2011). High school environments, stem orientations, and the gender gap in science and engineering degrees. Retrieved from http://ssrn.com/abstract=2008733.

  • 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.

    Article  Google Scholar 

  • Lewis, C. (2002). Lesson study: A handbook of teacher-led instructional change. Philadelphia: Research for Better Schools.

    Google Scholar 

  • Lewis, C. M., & Shah, N. (2012). Building upon and enriching grade four mathematics standards with programming curriculum. In Proceedings of the 43rd ACM technical symposium on computer science education (pp. 57–62). Raleigh, NC, USA: ACM.

  • Manches, A. A., & Plowman, L. (2017). Computing education in children’s early years: A call for debate. British Journal of Educational Technology, 48(1), 191–201.

    Article  Google Scholar 

  • Moreno-León, J., Robles, G., & Román-González, M. (2016). Code to learn: Where does it belong in the K-12 curriculum? Journal of Information Technology Education: Research, 15, 283–303. Retrieved from http://www.informingscience.org/Publications/3521.

  • Papert, S. (1993). Mindstorms: Children, computers, and powerful ideas (2nd ed.). New York: Basic Books, Inc.

    Google Scholar 

  • Papert, S. (2005). Teaching children thinking. Contemporary Issues In Technology & Teacher Education, 5(3), 353–365.

    Google Scholar 

  • Resnick, M. (2013). Learn to code, code to learn. EdSurge, May. Retrieved from https://www.edsurge.com/news/2013-05-08-learn-to-code-code-to-learn.

  • Richardson, V. (1990). Significant and worthwhile change in teaching practice. Educational Researchers, 19(7), 10–18.

    Article  Google Scholar 

  • Saeli, M., Perrenet, J., Jochems, W. M., & Zwaneveld, B. (2011). Teaching programming in secondary school: A Pedagogical content knowledge perspective. Informatics in Education, 10(1), 73–88.

    Google Scholar 

  • Saeli, M., Perrenet, J., Jochems, W. M. G., & Zwaneveld, B. (2012). Pedagogical content knowledge in teaching material. Journal of Educational Computing Research, 46(3), 267–293.

    Article  Google Scholar 

  • Sanjanaashree, P., Kumar, M. A., & Soman, K. (2014). Language learning for visual and auditory learners using scratch toolkit. In Proceedings of the Computer Communication and Informatics (ICCCI), 2014 International Conference on (pp. 1–5). Coimbatore, India: IEEE.

  • Shulman, L. S. (1986). Those who understand: Knowledge growth in teaching. Educational Researcher, 15(2), 4–14.

    Article  Google Scholar 

  • Shulman, L. S. (1987). Knowledge and teaching: Foundations of the new reform. Harvard Educational Review, 57(1), 1–22.

    Article  Google Scholar 

  • Simon, M. A., & Tzur, R. (1999). Explicating the teacher’s perspective from the researchers’ perspectives: Generating accounts of mathematics teachers’ practice. Journal for Research in Mathematics Education, 30, 252–264.

    Article  Google Scholar 

  • Smith, M. (2016). Computer Science for all. Retrieved from https://www.whitehouse.gov/blog/2016/01/30/computer-science-all.

  • Yadav, A., Gretter, S., Hambrusch, S., & Sands, S. (2016a). Expanding computer science education in schools: Understanding teachers experiences and challenges. Computer Science Education, 26(4), 235–254.

    Article  Google Scholar 

  • Yadav, A., Hong, H., & Stephenson, C. (2016b). Computational thinking for all: Pedagogical approaches to embedding 21st century problem solving in K-12 classrooms. TechTrends, 60, 565–568.

    Article  Google Scholar 

Download references

Funding

Funding was provided by the University of Redlands, School of Education’s Scholarly Project Fund.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Nicol R. Howard.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Howard, N.R. EdTech Leaders’ Beliefs: How are K-5 Teachers Supported with the Integration of Computer Science in K-5 Classrooms?. Tech Know Learn 24, 203–217 (2019). https://doi.org/10.1007/s10758-018-9371-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10758-018-9371-2

Keywords

Navigation