Abstract

Computational thinking (CT) has the potential to enhance learning when integrated into mathematical classroom activities. Teachers are being asked to include CT concepts in their core disciplines; however, there is an open question as to how best to equip teachers to integrate CT into their practice. Oftentimes teacher candidates enter math and science methods courses with emerging ideas of what CT might be but little formal experience with the construct (Yadav et al., 2014). Relatively little is understood about the most effective ways to support candidates’ understanding of CT, and how to support them in integrating CT into disciplinary instruction. In this paper, we describe a novel method of introducing teacher candidates to CT through a five-lesson module within the context of an existing pre-service teacher math and science methods course. We use an Experience First, Formalize Later format inspired by Stats Medic (2018) to help develop teacher candidates’ intuitions around CT primarily through firsthand experience and the roles it can play in their math and science classrooms. This paper presents the instructional materials for this innovative approach for integrating CT into a pre-service mathematics and science methods course. We will also present data from teaching these materials with a cohort of 14 teacher candidates. Collectively, this work contributes a novel strategy for integrating CT into pre-service methods courses and contributes to our understanding of the relationship between CT and the existing disciplines of K-12 math and science, especially as seen by teacher candidates entering the profession.

Keywords

References

• Abelson, H., & diSessa, A. A. (1986). Turtle geometry: The computer as a medium for exploring mathematics. The MIT Press.
• Bih, J. S., Weintrop, D., Walton, M., Elby, A., & Walkoe, J. (2020). Mutually supportive mathematics and computational thinking in a fourth-grade classroom. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, (Vol. 3, pp. 1389-1396). International Society of the Learning Sciences.
• Bingham, A. J., & Witkowsky, P. (2021). Deductive and inductive approaches to qualitative data analysis. In C. Vanover, P. Mihas, J. Saldaña, (Eds.), Analyzing and interpreting qualitative data: After the interview (pp. 133-146). SAGE.
• Brennan, K., & Resnick, M. (2012, April). Using artifact-based interviews to study the development of computational thinking in interactive media design [Paper presentation]. American Educational Research Association meeting 2012, Vancouver, BC, Canada.
• Cabrera, L. (2019). Teacher preconceptions of computational thinking: a systematic literature review. Journal of Technology and Teacher Education, 27(3), 305–333.
• Darling-Hammond, L., Hyler, M. E., Gardner, M., & Others. (2017). Effective teacher professional development. Learning Policy Institute. http://www.oregonrti.org/s/NO_LIF1.PDF
• Desimone, L. M. (2009). Improving impact studies of teachers’ professional development: Toward better conceptualizations and measures. Educational Researcher, 38(3), 181–199. https://doi.org/10.3102/0013189X08331140
• Dong, Y., Catete, V., Jocius, R., Lytle, N., Barnes, T., Albert, J., Joshi, D., Robinson, R., & Andrews, A. (2019). PRADA: A practical model for integrating computational thinking in K-12 education. Proceedings of the 50th ACM Technical Symposium on Computer Science Education, 906–912. https://doi.org/10.1145/3287324.3287431
• Gao, X. (2015). Promoting experiential learning in pre-service teacher education. Journal of Education for Teaching, 41(4), 435-438, DOI: 10.1080/02607476.2015.1080424
• Herbst, P., Chazan, D. & Lavu, S. (2019). Anotemos. Web-based collaborative software tool for the annotation of video. Disclosed to the Office of Technology Transfer, University of Michigan.
• Kaput, J., Noss, R., & Hoyles, C. (2002). Developing new notations for a learnable mathematics in the computational era. In L. D. English (Ed.), Handbook of International Research in Mathematics Education, (pp. 51–75). Routledge.
• Ketelhut, D. J., Mills, K., Hestness, E., Cabrera, L., Plane, J., & McGinnis, J. R. (2020). Teacher change following a professional development experience in integrating computational thinking into elementary science. Journal of Science Education and Technology, 29(1), 174–188. https://doi.org/10.1007/s10956-019-09798-4
• Lave, J., & Wenger, E. (1991). Situated learning: Legitimate peripheral participation. Cambridge University Press.
• Mouza, C., Yang, H., Pan, Y.-C., Ozden, S. Y., & Pollock, L. (2017). Resetting educational technology coursework for pre-service teachers: A computational thinking approach to the development of technological pedagogical content knowledge (TPACK). Australasian Journal of Educational Technology, 33(3). https://ajet.org.au/index.php/AJET/article/view/3521
• Munasinghe, B., Bell, T., & Robins, A. (2021, February 2). Teachers’ understanding of technical terms in a computational thinking curriculum. Australasian Computing Education Conference, 106-114. https://doi.org/10.1145/3441636.3442311
• Noss, R., & Hoyles, C. (1996). Windows on mathematical meanings: Learning cultures and computers. Kluwer.
• Papert, S. (1972). Teaching children to be mathematicians versus teaching about mathematics. International Journal of Mathematical Education in Science and Technology, 3(3), 249-262. https://doi.org/10.1080/0020739700030306
• Papert, S. A. (1980). Mindstorms: Children, computers, and powerful ideas. Basic books.
• Roschelle, J., Kaput, J., & Stroup, W. (2000). SIMCALC: Accelerating student engagement with the mathematics of change. In J. Roschelle, J. J. Kaput, W. Stroup (Eds.), Innovations in Science and Mathematics Education (pp. 47–75). Routledge.
• Schanzer, E., Fisler, K., Krishnamurthi, S., & Felleisen, M. (2015). Transferring skills at solving word problems from computing to algebra through Bootstrap. In Proceedings of the 46th ACM Technical symposium on computer science education (pp. 616-621). Association for Computing Machinery, United States. https://doi.org/10.1145/2676723.2677238
• 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
• Stats Medic. (2018). Experience first, formalize later. Stats Medic. https://www.statsmedic.com/post/experience-first-formalize-later
• Walton, M. & Walkoe, J. (2021). Computational Thinking as a Context for Ambitious Math Instruction. In de Vries, E., Hod, Y., & Ahn, J. (Eds.), Proceedings of the 15th International Conference of the Learning Sciences - ICLS 2021 (pp. 609-612). International Society of the Learning Sciences.
• Weintrop, D., Beheshti, E., Horn, M. S., Orton, K., Trouille, L., Jona, K., & Wilensky, U. (2014). Interactive assessment tools for computational thinking in high school STEM classrooms. In Intelligent Technologies for Interactive Entertainment: 6th International Conference, INTETAIN 2014, Chicago, IL, USA, July 9-11, 2014. Proceedings 6 (pp. 22-25). Springer International Publishing.
• 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. https://doi.org/10.1007/s10956-015-9581-5
• Weintrop, D., Walkoe, J., Walton, M., Bih, J., Moon, P., Elby, A., Bennett, B. & Kantzer, M. (2022). Sphero.Math: A computational thinking-enhanced fourth grade mathematics curriculum. In Computational Thinking in PreK-5: Empirical Evidence for Integration and Future Directions (pp. 39-46). https://doi.org/10.1145/3507951.3519286
• Wilensky, U. (1995). Paradox, programming, and learning probability: A case study in a connected mathematics framework. The Journal of Mathematical Behavior, 14(2), 253–280. https://doi.org/10.1016/0732-3123(95)90010-1
• Wing, J. M. (2006). Computational thinking. Communications of the ACM, 49(3), 33–35. https://doi.org/10.1145/1118178.1118215
• Yadav, A., Krist, C., Good, J., & Caeli, E. N. (2018). Computational thinking in elementary classrooms: measuring teacher understanding of computational ideas for teaching science. Computer Science Education, 28(4), 371–400. https://doi.org/10.1080/08993408.2018.1560550
• 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), 1-16. https://doi.org/10.1145/2576872
• Yeni, S., Grgurina, N., Hermans, F., Tolboom, J., & Barendsen, E. (2021, October). Exploring teachers’ PCK for computational thinking in context. In The 16th Workshop in Primary and Secondary Computing Education, (pp. 1-10). https://doi.org/10.1145/3481312.3481320

License

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.