Skip to main content

Advertisement

SpringerLink
Log in

Teacher implementation profiles for integrating computational thinking into elementary mathematics and science instruction

  • Published:
Education and Information Technologies Aims and scope Submit manuscript

Abstract

Incorporating computational thinking (CT) ideas into core subjects, such as mathematics and science, is one way of bringing early computer science (CS) education into elementary school. Minimal research has explored how teachers can translate their knowledge of CT into practice to create opportunities for their students to engage in CT during their math and science lessons. Such information can support the creation of quality professional development experiences for teachers. We analyzed how eight elementary teachers created opportunities for their students to engage in four CT practices (abstraction, decomposition, debugging, and patterns) during unplugged mathematics and science activities. We identified three strategies used by these teachers to create CT opportunities for their students: framing, prompting, and inviting reflection. Further, we grouped teachers into four profiles of implementation according to how they used these three strategies. We call the four profiles (1) presenting CT as general problem-solving strategies, (2) using CT to structure lessons, (3) highlighting CT through prompting, and (4) using CT to guide teacher planning. We discuss the implications of these results for professional development and student experiences.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Armoni, M. (2013). On teaching abstraction in computer science to novices. Journal of Computers in Mathematics and Science Teaching, 32(3), 265–284.

    Google Scholar 

  • Astrachan, O., Cuny, J., Stephenson, C., & Wilson, C. (2011). The CS10K project: Mobilizing the community to transform high school computing. In Proceedings of the 42nd ACM technical symposium on computer science education (pp. 85–86). New York: ACM.

  • Banilower, E. R., Smith, P. S., Weiss, I., Malzahn, K. A., Campbell, K. M., & Weis, A. M. (2013). Report of the 2012 national survey of science and mathematics education. Chapel Hill: Horizon Research, Inc..

    Google Scholar 

  • Battey, D., Neal, R. A., Leyva, L., & Adams-Wiggins, K. (2016). The interconnectedness of relational and content dimensions of quality instruction: Supportive teacher-student relationships in urban elementary mathematics classrooms. The Journal of Mathematical Behavior, 42, 1–19.

    Google Scholar 

  • Boaler, J. (2006). How a detracked mathematics approach promoted respect, responsibility, and high achievement. Theory Into Practice, 45(1), 40–46.

    Google Scholar 

  • Bocconi, S., Chioccariello, A., Dettori, G., Ferrari, A., & Engelhardt, K. (2016). Developing computational thinking in compulsory education: Implications for policy and practice. Resource document (EUR 28295 EN). https://doi.org/10.2791/792158.

  • Carroll, J. B. (1963). A model of school learning. Teachers College Record, 64, 722–733.

    Google Scholar 

  • Carroll, J. B. (1989). The Carroll model: A 25-year retrospective and prospective view. Educational Researcher, 18(1), 26–31.

    Google Scholar 

  • Common Core State Standards Initiative (CCSSI). (2010). Common Core State Standards for Mathematics. Resource document. CCSSI. http://www.corestandards.org/Math/.

  • Darling-Hammond, L. (2006). Constructing 21st-century teacher education. Journal of Teacher Education, 57(3), 300–314.

    Google Scholar 

  • Denning, P. J. (2017). Remaining trouble spots with computational thinking. Communications of the ACM, 60(6), 33–39.

    Google Scholar 

  • Duncan, C., Bell, T., & Atlas, J. (2017). What do the teachers think ? Introducing computational thinking in the primary school curriculum. In Proceedings of the Nineteenth Australasian Computing Education Conference (pp. 65–74). New York: ACM.

  • Finch, L., Shapiro, R. B., & Carstens, F. (2018). Teachers’ values in co-design of an art-science-computation unit. In J. Kay and R. Luckin (Eds.). Rethinking learning in the digital age: Making the learning sciences count, 13th International Conference of the Learning Sciences (ICLS) 2018, volume 1. London, UK: International Society of the Learning Sciences. https://doi.org/10.22318/cscl2018.663.

  • Goode, J., Chapman, G., & Margolis, J. (2012). Beyond curriculum: The exploring computer science program. ACM Inroads, 3(2), 47–53.

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  • Guskey, T. R. (2002). Professional development and teacher change. Teachers and Teaching: Theory and Practice, 8(3), 381–391.

    Google Scholar 

  • Hansen, A., Mavrikis, M., & Geraniou, E. (2016). Supporting teachers’ technological pedagogical content knowledge of fractions through co-designing a virtual manipulative. Journal of Mathematics Teacher Education, 19(2–3), 205–226.

    Google Scholar 

  • Hathaway, D., & Norton, P. (2019). The design document as a strategy for making design thinking visible: Innovating practice and embedding technology. In Proceedings of the Society for Information Technology & Teacher Education International Conference (pp. 1842–1849). Waynesville, NC: Association for the Advancement of Computing Education.

  • Hazzan, O. (2008). Reflections on teaching abstraction and other soft ideas. ACM SIGCSE Bulletin, 40(2), 40–43.

    Google Scholar 

  • Heritage, M., Kim, J., Vendlinksi, T., & Herman, J. (2009). From evidence to action: A seamless process in formative assessment? Educational Measurement: Issues and Practice, 28(3), 24–31.

    Google Scholar 

  • Hestness, E., Ketelhut, D. J., McGinnis, J. R., & Plane, J. (2018). Professional knowledge building within an elementary teacher professional development experience on computational thinking in science education. Journal of Technology and Teacher Education, 26(3), 411–435.

    Google Scholar 

  • Hiebert, J., Morris, A. K., Berk, D., & Jansen, A. (2007). Preparing teachers to learn from teaching. Journal of Teacher Education, 58(1), 47–61.

    Google Scholar 

  • 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 in Education, 82, 263–279.

    Google Scholar 

  • Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 37–42.

    Google Scholar 

  • Krist, C., Elby, A., Good, J., Gupta, A., Sohr, E. R., & Yadav, A. (2017). Integrating computational thinking strategies that support science inquiry: A case study from a summer PD. Paper presented at the Annual Meeting of American Educational Research Association, San Antonio, TX.

  • Loucks-Horsley, S., Stiles, K. E., Mundry, S., Love, N., & Hewson, P. W. (2010). Designing professional development for teachers of science and mathematics (3rd ed.). Thousand Oaks: Corwin.

    Google Scholar 

  • McGee, J. R., Polly, D., & Wang, C. (2012). Guiding teachers in the use of a standards-based mathematics curriculum: Teacher perceptions and subsequent instructional practices after an intensive professional development program. School Science and Mathematics, 113(1), 16–28.

    Google Scholar 

  • National Center for Education Statistics. (2019). Status and trends in the education of racial and ethnic groups (NCES 2019–038). U.S. Department of Education. Washington, DC: National Center for Education Statistics. Retrieved from https://nces.ed.gov/programs/raceindicators/index.asp.

  • Next Generation Science Standards (NGSS). (2013). Next Generation Science Standards. Resource document. Achieve. https://www.nextgenscience.org/search-standards.

  • Priestley, M., Edwards, R., Priestley, A., & Miller, K. (2012). Teacher agency in curriculum making: Agents of change and spaces for manoeuvre. Curriculum Inquiry, 42(2), 191–214.

    Google Scholar 

  • Rich, K. M., Strickland, C., Binkowski, T. A., Moran, C., & Franklin, D. (2017). K-8 learning trajectories derived from research literature: Sequence, repetition, conditionals. In Proceedings of the 2017 ACM Conference on International Computing Education Research (pp. 182–190). New York: ACM.

  • Rich, K. M., Yadav, A., & Schwarz, C. (2019). Computational thinking, mathematics, and science: Elementary teachers' perspectives on integration. Journal of Technology and Teacher Education, 27(2), 165-205.

  • Ryu, S., & Sandoval, W. A. (2012). Improvements to elementary children’s epistemic understanding from sustained argumentation. Science Education, 96(3), 488–526.

    Google Scholar 

  • Sherin, M. G., & van Es, E. A. (2009). Effects of video club participation on teachers’ professional vision. Journal of Teacher Education, 60(1), 20–37.

    Google Scholar 

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

    Google Scholar 

  • Smith, M. S., Bill, V., & Hughes, E. K. (2008). Thinking through a lesson: Successfully implementing high-level tasks. Mathematics Teaching in the Middle School, 14(3), 132–138.

    Google Scholar 

  • Toom, A. (2017). Teachers’ professional and pedagogical competencies: A complex divide between teacher work, teacher knowledge and teacher education. In D. J. Clandinin & J. Husu (Eds.), The SAGE handbook of research on teacher education (pp. 803–819). London: Sage Publications.

    Google Scholar 

  • van Zanten, M., & van den Heuvel-Panhuizen, M. (2018). Opportunity to learn problem solving in dutch primary school mathematics textbooks. ZDM: The International Journal on Mathematics Education, 50(5), 827–838.

    Google Scholar 

  • Weintrop, D., Beheshti, E., Horn, M., Orton, K., Jona, K., Trouille, L., & Wilensky, U. (2016). Defining computational thinking for mathematics and science classrooms. Journal of Science Education and Technology, 25(1), 127–147.

    Google Scholar 

  • Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35.

    Google Scholar 

  • 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.

    Google Scholar 

  • Yadav, A., Stephenson, C., & Hong, H. (2017). Computational thinking for teacher education. Communications of the ACM, 60(4), 55–62.

    Google Scholar 

  • Yadav, A., Krist, C., Good, J., & Caeli, E. (2018). Computational thinking in elementary classrooms: Measuring teacher understanding of computational ideas for teaching science. Computer Science Education, 28(4), 371–400.

    Google Scholar 

  • Yadav, A., Larimore, R., Rich, K., & Schwarz, C. (2019). Integrating computational thinking in elementary classrooms: Introducing a toolkit to support teachers. In Society for Information Technology & Teacher Education International Conference (pp. 347-350). Association for the Advancement of Computing in Education (AACE).

Download references

Funding

This work is supported by the National Science Foundation under grant number 1738677. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.

Author information

Authors and Affiliations

  1. Michigan State University College of Education, East Lansing, MI, USA

    Kathryn M. Rich, Aman Yadav & Rachel A. Larimore

Authors
  1. Kathryn M. Rich
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Aman Yadav
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Rachel A. Larimore
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Kathryn M. Rich.

Ethics declarations

The authors declare that they have no conflict of interest. The study was reviewed and approved by the Institutional Review Board at the university at which the study was conducted. Informed consent was obtained from all teacher participants. No students were identified and no student data was analyzed.

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

Table 9 Narrative description of CT opportunities provided by each teacher
Full size table

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rich, K.M., Yadav, A. & Larimore, R.A. Teacher implementation profiles for integrating computational thinking into elementary mathematics and science instruction. Educ Inf Technol 25, 3161–3188 (2020). https://doi.org/10.1007/s10639-020-10115-5

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-020-10115-5

Keywords

Search

Navigation