Sociomathematical Norms for Integrating Coding and Modeling with Elementary Science: A Dialogical Approach


In recent years, the field of education has challenged researchers and practitioners to incorporate computing as an essential focus of K-12 STEM education. Integrating computing within K-12 STEM supports learners of all ages in codeveloping and using computational thinking in existing curricular contexts alongside practices essential for developing mathematical and scientific expertise. In this paper, we present findings from a design-based, microgenetic study in which an agent-based programming and computational modeling platform—ViMAP—was integrated with existing elementary science and math curricula through lessons co-designed and taught by the classroom teacher across a period of seven months. We present a dialogical re-positioning of coding, where disciplinarily grounded meanings of code emerge through the construction of computational utterances––i.e., computer models as well as complementary conversations and physical models that serve as mathematical and scientific explanations––through the use of socio-mathematical norms.

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  1. Arnovsky, J. (2008). Wild tracks!: A guide to nature[s footprints. New York, NY: Sterling Children’s Books.

  2. Bakhtin, M. M. (1983). The dialogic imagination: Four essays. Austin, TX: University of texas Press.

    Google Scholar 

  3. Basu, S., Dickes, A., Kinnebrew, J. S., Sengupta, P., & Biswas, G. (2013). CTSiM: A computational thinking environment for learning science through simulation and modeling. In CSEDU (pp. 369–378).

  4. Basu, S., Biswas, G., Sengupta, P., Dickes, A., Kinnebrew, J. S., & Clark, D. (2016). Identifying middle school students’ challenges in computational thinking-based science learning. Research and practice in technology enhanced learning, 11(1), 13.

    Article  Google Scholar 

  5. Brennan, K., & Resnick, M. (2012). New frameworks for studying and assessing the development of computational thinking. In Proceedings of the 2012 annual meeting of the American Educational Research Association, Vancouver, Canada (pp. 1–25).

  6. Cobb, P., Yackel, E., & Wood, T. (1989). Young children’s emotional acts while engaged in mathematical problem solving. In Affect and mathematical problem solving (pp. 117–148). New York, NY: Springer.

    Google Scholar 

  7. Cobb, P., Wood, T., Yackel, E., & McNeal, B. (1992). Characteristics of classroom mathematics traditions: An interactional analysis. American Educational Research Journal, 29(3), 573–604.

    Article  Google Scholar 

  8. Cobb, P., Gresalfi, M., & Hodge, L. L. (2009). A design research perspective on the identities that students are developing in mathematics classrooms. Transformation of knowledge through classroom interaction, 223–243.

  9. Coburn, C. E., Toure, J., & Yamashita, M. (2009). Evidence, interpretation, and persuasion: Instructional decision making at the district central office. Teachers College Record, 111(4), 1115–1161.

    Google Scholar 

  10. Coburn, C. E., Penuel, W. R., & Geil, K. E. (2013). Research-practice partnerships: A strategy for leveraging research for educational improvement in school districts. New York: William T. Grant Foundation.

    Google Scholar 

  11. Coburn, C. E., & Penuel, W. R. (2016). Research–practice partnerships in education: Outcomes, dynamics, and open questions. Educational Researcher, 45(1), 48–54.

    Article  Google Scholar 

  12. Dickes, A. & Farris, A. (2019). Beyond Isolated Competencies: Development of Computational Literacy in an Elementary Science Classroom. In: Sengupta, P., Shanahan, M.-C., & Kim, B. (Eds). Critical, Transdisciplinary and Embodied Approaches in STEM Education . (pp 131 - 150). Springer: New York.

  13. Dickes, A., Sengupta, P., Farris, A. V., & Basu, S. (2016). Development of mechanistic reasoning and multi-level explanations in 3rd grade biology using multi-agent based models. Science Education, 100(4), 734–776.

    Article  Google Scholar 

  14. diSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: Children’s meta-representational expertise. The Journal of Mathematical Behavior, 10(2), 117–160.

    Google Scholar 

  15. Farris, A. V., & Sengupta, P. (2016). Democratizing children's computation: Learning computational science as aesthetic experience. Educational Theory, 66(1-2), 279–296.

    Article  Google Scholar 

  16. Ford, M. J., & Forman, E. A. (2006). Redefining disciplinary learning in classroom contexts. Review of Research in Education, 30, 1–32.

    Article  Google Scholar 

  17. Geertz, C. (1983). Local knowledge: Further essays in interpretive anthropology (Vol. 5110). New York: Basic Books.

    Google Scholar 

  18. Glaser, B. G., & Strauss, A. L. (1967). The discovery of grounded theory: Strategies for qualitative research. Chicago: Aldire.

    Google Scholar 

  19. Guzdial, M. (2006). Software-Realized Scaffolding to Facilitate Programming for Science Learning. Interactive Learning Environments, 4(1), 001–044.

    Article  Google Scholar 

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

    Article  Google Scholar 

  21. Harel, I. E., & Papert, S. E. (1991). Constructionism. Ablex Publishing. di Sessa, A. A. (1991). An overview of Boxer. Journal of Mathematical Behavior, 10(1), 3-15.

  22. Huberman, M. A., & Miles, M. B. (1994). Data management and analysis methods. In N. Denzin & Y. S. Lincoln (Eds.), Handbook of Qualitative Research. Thousand Oaks, CA: Sage.

  23. Ilic, U., Haseski, H. İ., & Tugtekin, U. (2018). Publication Trends Over 10 Years of Computational Thinking Research. Contemporary Educational Technology, 9(2), 131–153.

    Article  Google Scholar 

  24. Kafai, Y., & Harel, I. (1991). Children learning through consulting: When mathematical ideas, knowledge of programming and design, and playful discourse are intertwined. Constructionism, 110–140.

  25. Kafai, Y. B., & Burke, Q. (2013). The social turn in K-12 programming: moving from computational thinking to computational participation. In Proceeding of the 44th ACM technical symposium on computer science education (pp. 603–608). ACM.

  26. Kafai, Y., & Harel, I. (1991). Learning through design and teaching: Exploring social and collaborative aspects of constructionsm.

  27. Lead States, N. G. S. S. (2013). Next generation science standards: for states, by states. Washington, DC: National Academies Press

    Google Scholar 

  28. Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickson, J., et al. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37.

    Article  Google Scholar 

  29. Lee, I., Martin, F., & Apone, K. (2014). Integrating computational thinking across the K--8 curriculum. ACM Inroads, 5(4), 64–71.

    Article  Google Scholar 

  30. Lehrer, R., & Schauble, L. (2000). Developing model-based reasoning in mathematics and science. Journal of Applied Developmental Psychology, 21(1), 39–48.

    Article  Google Scholar 

  31. Lehrer, R., & Schauble, L. (2006). Cultivating Model-Based Reasoning in Science Education. Cambridge: Cambridge University Press.

    Google Scholar 

  32. Lehrer, R., Schauble, L., Strom, D., & Pligge, M. (2001). Similarity of form and substance: Modeling material kind. Cognition and instruction: Twenty-five years of progress, (pp. 39–74).

  33. Lehrer, R., Schauble, L., & Lucas, D. (2008). Supporting development of the epistemology of inquiry. Cognitive Development, 23(4), 512–529.

    Article  Google Scholar 

  34. Lesh, R. A., & Doerr, H. M. (2003). Beyond constructivism: Models and modeling perspectives on mathematics problem solving, learning, and teaching. Abingdon: Routledge.

    Google Scholar 

  35. McClain, K., & Cobb, P. (2001). An analysis of development of sociomathematical norms in one first-grade classroom. Journal for Research in Mathematics Education, 32(3), 236–266.

    Article  Google Scholar 

  36. NRC (National Research Council). (2007). Tools and methods for estimating populations at risk from natural disasters and complex humanitarian crises. Report by the National Academy of Sciences (p. 264). Washington, DC: National Academy Press.

    Google Scholar 

  37. Nersessian, N. (2008). Model-based reasoning in scientific practice. In Teaching scientific inquiry. Brill Sense, 57–79.

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

    Google Scholar 

  39. Peppler, K., Danish, J., Zaitlen, B., Glosson, D., Jacobs, A., & Phelps, D. (2010). BeeSim: leveraging wearable computers in participatory simulations with young children. In Proceedings of the 9th International Conference on Interaction Design and Children (pp. 246–249). ACM.

  40. Pickering, A. (1995). The mangle of practice: Time, agency, and science. Chicago: University of Chicago Press.

    Google Scholar 

  41. Resnick, M., Maloney, J., Monroy-Hernández, A., Rusk, N., Eastmond, E., Brennan, K., ... & Kafai, Y. (2009). Scratch: programming for all. Communications of the ACM, 52(11), 60-67.

  42. Roschelle, J., & Teasley, S. D. (1995). The construction of shared knowledge in collaborative problem solving. In Computer supported collaborative learning (pp. 69–97). Berlin, Heidelberg: Springer.

    Google Scholar 

  43. Schwarz, C. V., & White, B. Y. (2005). Metamodeling knowledge: Developing students’ understanding of scientific modeling. Cognition and Instruction, 23(2), 165–205.

    Article  Google Scholar 

  44. Sengupta, P., & Wright, M. (2010). ViMAP. In Computer software. Nashville: Mind, Matter & Media Lab, Vanderbilt University.

    Google Scholar 

  45. Sengupta, P., Kinnebrew, J. S., Basu, S., Biswas, G., & Clark, D. (2013). Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Education and Information Technologies, 18(2), 351–380.

    Article  Google Scholar 

  46. Sengupta, P., Dickes, A., Farris, A. V., Karan, A., Martin, D., & Wright, M. (2015). Education: Programming in K-12 science classrooms. Communications of the ACM, 58(11), 33–35.

  47. Sengupta, P., Dickes, A., & Farris, A. V. (2018). Toward a Phenomenology of Computational Thinking in STEM. In M. S. Khine (Ed.), Computational Thinking in STEM: Research Highlights (pp. 49–72). New York: Springer.

    Google Scholar 

  48. Sengupta, P., Dickes, A., & Farris, A. V. (In Press). Voicing Code in STEM: A Dialogical Imagination. Cambridge, MA: MIT Press.

  49. Severance, S., Penuel, W. R., Sumner, T., & Leary, H. (2016). Organizing for teacher agency in curricular co-design. The Journal of the Learning Sciences.

  50. Sherin, B., diSessa, A. A., & Hammer, D. (1993). Dynaturtle revisited: Learning physics through collaborative design of a computer model. Interactive Learning Environments, 3(2), 91–118.

    Article  Google Scholar 

  51. Spillane, J. P. (1998). State policy and the non-monolithic nature of the local school district: Organizational and professional considerations. American Educational Research Journal, 35(1), 33–63.

    Article  Google Scholar 

  52. StarLogo TNG website; Accessed 30 July 2014.

  53. Todorov, T. (1984). Mikhail Bakhtin: the dialogical principle (Vol. 13). Manchester University Press.

  54. 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.

    Article  Google Scholar 

  55. Wilensky, U. (1999). {NetLogo}.

  56. Wilkerson-Jerde, M., Wagh, A., & Wilensky, U. (2015). Balancing curricular and pedagogical needs in computational construction kits: Lessons from the deltatick project. Science Education, 99(3), 465–499.

    Article  Google Scholar 

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

    Article  Google Scholar 

  58. Yackel, E., & Cobb, P. (1996). Sociomathematical norms, argumentation, and autonomy in mathematics. Journal for Research in Mathematics Education, 22(4), 390–408.

    Google Scholar 

  59. Yackel, E., Cobb, P., & Wood, T. (1991). Small-group interactions as a source of learning opportunities in second-grade mathematics. Journal for research in mathematics education, 22(5), 390–408.

    Article  Google Scholar 

  60. Yin, R. K. (1994). Discovering the future of the case study. Method in evaluation research. Evaluation practice, 15(3), 283–290.

    Article  Google Scholar 

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The authors gratefully acknowledge the contributions of Cherifa Ghassoul, Emily Brutcher and Leah Embry.


This project was partially supported by US National Science Foundation (NSF CAREER Award #1150230) and funds from the Imperial Foundation (Canada).

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Correspondence to Amanda Catherine Dickes.

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Dickes, A.C., Farris, A.V. & Sengupta, P. Sociomathematical Norms for Integrating Coding and Modeling with Elementary Science: A Dialogical Approach. J Sci Educ Technol 29, 35–52 (2020).

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  • Coding
  • Modeling
  • Elementary science
  • Computational thinking
  • Sociomathematical norms
  • Science education