Skip to main content
Log in

Balancing the Environment: Computational Models as Interactive Participants in a STEM Classroom

  • Published:
Journal of Science Education and Technology Aims and scope Submit manuscript

A Correction to this article was published on 21 December 2019

This article has been updated


This paper describes the work done by sixth grade students to achieve and sustain productive and personally meaningful lines of inquiry with computational models. The capacity to frame interactions with tools as dialogic exchanges with co-participants is a productive practice for disciplinary engagement in science and for computational thinking (Chandrasekharan and Nersessian 2015; Dennet 1989; Latour 1993; Pickering 1995). We propose that computational models have unique affordances for dialogic interaction because they are probabilistic and iteratively executable, features that provide an entry point for students to adopt stances that treat computational models as participants. Our analysis reveals that existing patterns in students’ social interactions are resources for interacting flexibly with computational tools as participants. In particular, we found that students treated computational models as participants in three ways: (1) as conversational peers, (2) as co-constructors of lines of inquiry, and (3) as projections of students’ agency and identity. Our data also demonstrate that students take on flexible, rather than fixed, stances toward computational participants. These stances parallel scientists’ interactions with non-human entities, which often involve treating tools as agentive participants in inquiry (Latour 1999; Pickering 1995), affording students a pathway to practices at the intersection of disciplinary engagement and computational thinking.

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

Access this article

Subscribe and save

Springer+ Basic
EUR 32.99 /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.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Similar content being viewed by others

Change history

  • 21 December 2019

    The original version of this article unfortunately contained a mistake. The citation and bibliographic information of this reference is missing in the original article.


  • Ackermann, E. (2012). Perspective-taking and object construction: Two keys to learning. In Constructionism in practice (pp. 39–50). Routledge.

  • Berland, L. K., Schwarz, C. V., Krist, C., Kenyon, L., Lo, A. S., & Reiser, B. J. (2015). Epistemologies in practice: making scientific practices meaningful for students. Journal of Research in Science Teaching, 53(2), 1082–1112.

    Google Scholar 

  • Brady, C., Holbert, N., Soylu, F., Novak, M., & Wilensky, U. (2015). Sandboxes for model-based inquiry. Journal of Science Education and Technology, 24(2), 265–286.

  • Burke, Q., O’Byrne, W. I., & Kafai, Y. B. (2016). Computational participation. Journal of Adolescent & Adult Literacy, 59(4), 371–375.

    Google Scholar 

  • Chandrasekharan, S., & Nersessian, N. J. (2015). Building cognition: the construction of computational representations for scientific discovery. Cognitive Science, 39, 1727–1763.

    Google Scholar 

  • Chandrasekharan, S., & Nersessian, N. J. (2017). Rethinking correspondence: how the process of constructing models leads to discoveries and transfer in the bioengineering sciences. Synthese, 48(6), 1–30.

    Google Scholar 

  • Cobb, P., Confrey, J., diSessa, A., Lehrer, R., & Schauble, L. (2003). Design experiments in educational research. Educational Researcher, 32(1), 9–13.

    Google Scholar 

  • Dennett, D. C. (1989). The intentional stance. Cambridge: MIT press.

    Google Scholar 

  • Dickes, A. C., & Sengupta, P. (2013). Learning natural selection in 4th grade with multi-agent-based computational models. Research in Science Education, 43(3), 921–953.

    Google Scholar 

  • Dillenbourg, P., Järvelä, S., & Fischer, F. (2009). The evolution of research on computer-supported collaborative learning. In N. Balacheff, S. Ludvigsen, T. de Jong, A. Lazonder, & S. Barnes (Eds.), Technology-enhanced learning. Dordrecht: Springer.

    Google Scholar 

  • diSessa, A., Hammer, D., Sherin, B., & Kolpakowski, T. (1991). Inventing graphing: meta-representational expertise in children. The Journal of Mathematical Behavior, 10(2), 117–160.

    Google Scholar 

  • Ellis, N., & Larsen-Freeman, D. (Eds.). (2009). Language as a complex adaptive system. Oxford: Wiley.

    Google Scholar 

  • Epstein, J., & Axtell, R. (1996). Growing artifical societies: social science from the bottom up. Washington: Brookings Institution Press.

    Google Scholar 

  • Farris, A. V., & Sengupta, P. (2014). Perspectival computational thinking for learning physics: a case study of collaborative agent-based modeling. Proceedings of the 12th International Conference of the Learning Sciences. (ICLS 2014), pp 1102 - 1107.

  • Fox Keller, E. (1983). A feeling for the organism, 10th anniversary edition: the life and work of Barbara McClintock. New York: Henry Holt and Company, LLC..

    Google Scholar 

  • Fox Keller, E. (2003). Models, simulation, and computer experiments. In H. Radder (Ed.), The philosophy of scientific experimentation (pp. 198–215). Pittsburgh: University of Pittsburgh Press.

    Google Scholar 

  • Gee, J. P. (2014). An introduction to discourse analysis: theory and method (4th ed.). New York: Routledge.

    Google Scholar 

  • Goffman, E. (1981). Footing. In E. Goffman (Ed.), Forms of talk (pp. 124–159). University of Pennsylvania Press.

  • Goodwin, C. (2007). Interactive footing. In E. Holt & R. Clift (Eds.), Reporting talk: Reported speech in interaction (pp. 16–64). Cambridge.

  • Goodwin, C. (2017). Co-operative action (learning in doing: social, cognitive, and computational perspectives). Cambridge: Cambridge University Press.

    Google Scholar 

  • Guo, Y., Wagh, A., Brady, C., Levy, S. T., Horn, M. S., & Wilensky, U. (2016). Frogs to think with: Improving Students' computational thinking and understanding of evolution in a code-first learning environment. Proceedings of the 15th International Conference of ACM SIGCHI Interaction Design and Children (IDC 2016). (pp. 246–254).

  • Horn, M., Brady, C., Hjorth, A., Wagh, A., & Wilensky, U. (2014). Frog pond: A code-first learning environment on evolution and natural selection. In Proceedings of Interaction Design and Children (IDC'14).

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

  • Kearney, M. (2004). Classroom use of multimedia-supported predict–observe–explain tasks in a social constructivist learning environment. Research in Science Education, 34(4), 427–453.

    Google Scholar 

  • Klopfer, E. (2003). Technologies to support the creation of complex systems models--using StarLogo software with students. Biosystems, 71(1-2), 111–122.

    Google Scholar 

  • Latour, B. (1993). Pasteur on lactic acid yeast: a partial semiotic analysis. In Configurations, 1.1 (pp. 129–146). Baltimore: Johns Hopkins University Press.

    Google Scholar 

  • Latour, B. (1999). Pandora’s hope: essays on the reality of science studies. Cambridge: Harvard University Press.

    Google Scholar 

  • Latour, B. (2005). Reassembling the social: an introduction to actor-network-theory. Oxford: Oxford University Press.

    Google Scholar 

  • Lee, I., Martin, F., Denner, J., Coulter, B., Allan, W., Erickso, J., Malyn-Smith, J., & Werner, L. (2011). Computational thinking for youth in practice. ACM Inroads, 2(1), 32–37.

    Google Scholar 

  • Leelawong, K., & Biswas, G. (2008). Designing learning by teaching agents: the Betty’s Brain system. International Journal of Artificial Intelligence in Education, 18(3), 181–208.

    Google Scholar 

  • Moreno-Armella, L., & Brady, C. (2018). Technological Supports for Mathematical Thinking and Learning: Co-action and Designing to Democratize Access to Powerful Ideas. In Uses of Technology in Primary and Secondary Mathematics Education (pp. 339–350). Springer, Cham.

  • National Science Foundation [NSF]. (2019). Future of work at the human-technology frontier. Retrieved February 22, 2019, from

  • Nemirovsky, R., Tierney, C., & Wright, T. (1998). Body motion and graphing. Cognition and Instruction, 16(2), 119–172.

    Google Scholar 

  • Next Generation Science Standards Lead States. (2013). Next generation science standards: for states, by states. Washington, DC: National Academies Press.

    Google Scholar 

  • Norris, S., & Jones, R. (2005). Discourse in action: Introducing mediated discourse analysis. New York: Routledge.

    Google Scholar 

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

    Google Scholar 

  • Pickering, A. (1995). The mangle of practice: time, agency and science. In American Journal of Sociology. Chicago: University of Chicago Press.

    Google Scholar 

  • Pierson, A. E., Clark, D. B., & Sherard, M. K. (2017). Learning progressions in context: Tensions and insights from a semester‐long middle school modeling curriculum. Science Education, 101(6), 1061–1088.

  • Railsback, S. F., Lytinen, S. L., & Jackson, S. K. (2006). Agent-based simulation platforms: review and development recommendations. Simulation, 82(9), 609–523.

    Google Scholar 

  • Resnick, M. (1994). Turtles, termites and traffic jams: explorations in massively parallel microworlds. Cambridge: MIT Press.

    Google Scholar 

  • Rienties, B., Giesbers, B., Tempelaar, D., Lygo-Baker, S., Segers, M., & Gijselaers, W. (2012). The role of scaffolding and motivation in CSCL. Computers & Education, 59(3), 893–906.

    Google Scholar 

  • Salk, J. (1983). Anatomy of reality: merging of intuition and reason. New York: Columbia University Press.

    Google Scholar 

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

  • Sengupta, P., Dickes, A., & Farris, A. (2018). Toward a phenomenology of computational thinking in STEM education. In Computational Thinking in the STEM Disciplines (pp. 49–72). Cham: Springer.

    Google Scholar 

  • Sengupta, P., Dickes, A., & Farris, A.V. (2020). Voicing code in STEM: A dialogical imagination. MIT Press. Cambridge, MA (forthcoming)

  • 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.

  • Sengupta, P., & Wilensky, U. (2009). Learning electricity with NIELS: thinking with electrons and thinking in levels. International Journal of Computers for Mathematical Learning, 14(1), 21–50.

    Google Scholar 

  • Valdés, G. (2015). Latin@s and the intergenerational continuity of Spanish: the challenges of curricularizing language. International Multilingual Research Journal, 9(4), 253–273.

    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 

  • Wilensky, U., & Reisman, K. (2006). Thinking like a wolf, a sheep, or a firefly: learning biology through constructing and testing computational theories—an embodied modeling approach. Cognition and Instruction, 24(2), 171–209.

    Google Scholar 

  • Wilensky, U., & Resnick, M. (1999). Thinking in levels: a dynamic systems perspective to making sense of the world. Journal of Science Education and Technology, 8(1), 3–19.

    Google Scholar 

  • Wilkerson-Jerde, M. H., Gravel, B. E., & Macrander, C. A. (2015). Exploring shifts in middle school learners’ modeling activity while generating drawings, animations, and computational simulations of molecular diffusion. Journal of Science Education and Technology, 24, 396–415.

    Google Scholar 

  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society of London A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725.

    Google Scholar 

  • Yoon, S., Klopfer, E., Anderson, E., Koehler-Yom, J., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub, H., Oztok, M., Evans, C., & Goh, S. (2016). Designing computer-supported complex systems curricula for the Next Generation Science Standards in high school science classrooms. Systems, 4(38), 1–18.

    Google Scholar 

  • Yoon, S., Anderson, E., Koehler-Yom, Evans, C., Park, M., Sheldon, J., Schoenfeld, I., Wendel, D., Scheintaub, H., & Klopfer, E. (2017). Teaching about complex systems is not simple matter: building effective professional development for computer-supported complex systems instruction. Instructional Science, 45(1), 99–121.

    Google Scholar 

Download references


This study was supported by the National Science Foundation through grant 1742138 to Vanderbilt University.

Author information

Authors and Affiliations


Ethics declarations

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional review board VU FWA#00024139 and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. All applicable international, national, and/or institutional guidelines for the care and use of animals were followed.

Conflict of Interest

The authors declare that they have no conflict of interest.


The opinions expressed are those of the authors and do not represent views of the National Science Foundation.

Additional information

Publisher’s Note

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

The original version of this article was revised: The original version of this article unfortunately contained a mistake. The citation and bibliographic information of this reference is missing in the original article. “Sengupta, P., Dickes, A., & Farris, A.V. (2020). Voicing code in STEM: A dialogical imagination. MIT Press. Cambridge, MA (forthcoming)” and should be cited in p. 3, fourth paragraph of the section heading “Computational Models as Participants”. In this paper, we describe the work done by sixth grade students to adopt productive stances toward computational models, including stances that treat computational tools as participants in interaction and in inquiry. As described above, flexibly partnering with computational participants is becoming an essential skill within emerging STEM fields. Our data suggest that students leverage existing patterns in social interaction as resources for interacting with computational models as participants in this classroom. From a practical perspective, our data demonstrate that our students interact with their models as conversational peers. The utterances that students produce reflect the intentions and ideas of the students but are also shaped in part by the computational models (Sengupta et al, forthcoming). In combination with the probabilistic nature of the models and the students’ inexperience with the environment, these participation structures position the models as co-constructors of new lines of inquiry.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Pierson, A.E., Brady, C.E. & Clark, D.B. Balancing the Environment: Computational Models as Interactive Participants in a STEM Classroom. J Sci Educ Technol 29, 101–119 (2020).

Download citation

  • Published:

  • Issue Date:

  • DOI: