Skip to main content

Advertisement

Log in

Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework

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

Abstract

Computational thinking (CT) draws on concepts and practices that are fundamental to computing and computer science. It includes epistemic and representational practices, such as problem representation, abstraction, decomposition, simulation, verification, and prediction. However, these practices are also central to the development of expertise in scientific and mathematical disciplines. Recently, arguments have been made in favour of integrating CT and programming into the K-12 STEM curricula. In this paper, we first present a theoretical investigation of key issues that need to be considered for integrating CT into K-12 science topics by identifying the synergies between CT and scientific expertise using a particular genre of computation: agent-based computation. We then present a critical review of the literature in educational computing, and propose a set of guidelines for designing learning environments on science topics that can jointly foster the development of computational thinking with scientific expertise. This is followed by the description of a learning environment that supports CT through modeling and simulation to help middle school students learn physics and biology. We demonstrate the effectiveness of our system by discussing the results of a small study conducted in a middle school science classroom. Finally, we discuss the implications of our work for future research on developing CT-based science learning environments.

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

Access this article

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
Fig. 6
Fig. 7

References

  • ACM K-12 Taskforce. (2003). A Model Curriculum for K-12 Computer Science: Final Report of the ACM K-12 Task Force Curriculum Committee. New York, NY: CSTA.

    Google Scholar 

  • Aristotle (350 BCE/2002) Nichomachean ethics. New York: Oxford University Press.

  • Basu, S., Sengupta, P., & Biswas, G. (In Review). A scaffolding framework to support learning in multi-agent based simulation environments. Research in Science Education.

  • Basu, S., Kinnebrew, J., Dickes, A., Farris, A. V., Sengupta, P., Winger, J., & Biswas, G. (2012). A Science Learning Environment using a Computational Thinking Approach. In: Proceedings of the 20th International Conference on Computers in Education, Singapore.

  • Blikstein, P., & Wilensky, U. (2009). An atom is known by the company it keeps: A constructionist learning environment for materials science using Agent-Based Modeling. International Journal of Computers for Mathematical Learning, 14, 81–119.

    Article  Google Scholar 

  • Bravo, C., van Joolingen, W. R., & deJong, T. (2006). Modeling and simulation in inquiry learning: Checking solutions and giving advice. Simulation, 82(11), 769–784.

    Article  Google Scholar 

  • Chi, M. T. H. (2005). Common sense conceptions of emergent processes: Why some misconceptions are robust. Journal of the Learning Sciences, 14, 161–199.

    Article  MathSciNet  Google Scholar 

  • Chi, M. T. H., Slotta, J. D., & de Leeuw, N. (1994). From things to processes: A theory of conceptual change for learning science concepts. Learning and Instruction, 4, 27–43.

    Article  Google Scholar 

  • Chinn, C. A., & Brewer, W. F. (1993). The role of anomalous data in knowledge acquisition: A theoretical framework and implications for science instruction. Review of Educational Research, 63, 1–49.

    Google Scholar 

  • Conway, M. (1997). Alice: Easy to Learn 3D Scripting for Novices, Technical Report, School of Engineering and Applied Sciences, University of Virginia, Charlottesville, VA.

  • Corcoran, T., Mosher, F., & Rogat, A. (2009). Learning progressions in science: An evidence-based approach to reform (RR-63). Philadelphia, PA: Consortium for Policy Research in Education.

    Google Scholar 

  • Cross, N. (2004). Expertise in design: An overview. Design Studies, 25, 427–441.

    Article  Google Scholar 

  • Dickes, A., & Sengupta, P. (2012). Learning Natural Selection in 4th Grade with Multi Agent-Based Computational Models. Research in Science Education. doi:10.1007/s11165-012-9293-2.

  • diSessa, A. A. (1985). A principled design for an integrated computational environment. Human-Computer Interaction, 1(1), 1–47.

    Article  Google Scholar 

  • diSessa, A. A. (1993). Toward an epistemology of physics. Cognition and Instruction, 10(2/3), 105–225.

    Article  Google Scholar 

  • diSessa, A. A. (2000). Changing minds: Computers, learning, and literacy. Cambridge, MA: MIT Press.

    Google Scholar 

  • diSessa, A. A. (2004). Metarepresentation: Native competence and targets for instruction. Cognition and Instruction, 22(3), 293–331.

    Article  Google Scholar 

  • diSessa, A. A. (2001). Changing minds: Computers, learning, and literacy. The MIT Press.

  • diSessa, A. A., & Abelson, H. (1986). BOXER: A reconstructible computational medium. Communications of ACM, 29(9), 859–868.

    Article  Google Scholar 

  • diSessa, A. A., Abelson, H., & Ploger, D. (1991a). An overview of boxer. Journal of Mathematical Behavior, 10(1), 3–15.

    Google Scholar 

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

    Google Scholar 

  • Driver, R., Newton, P., & Osborne, J. (2000). Establishing the norms of scientific argumentation in classrooms. Science Education, 84(3), 287–313.

    Article  Google Scholar 

  • Duschl, R. (2008). Science education in three part harmony: Balancing conceptual, epistemic and social learning goals. In J. Green, A. Luke, & G. Kelly (Eds.), Review of research in education (Vol. 32, pp. 268–291). Washington, DC: AERA.

    Google Scholar 

  • Duschl, R. A., & Osborne, J. (2002). Supporting and promoting argumentation discourse in science education. Studies in Science Education, 38, 39–72.

    Article  Google Scholar 

  • Dykstra, D. I., Jr., & Sweet, D. R. (2009). Conceptual development about motion and force in elementary and middle school students. American Journal of Physics, 77(5), 468–476.

    Google Scholar 

  • Edelson, D. C. (2001). Learning-for-use: A framework for the design of technology-supported inquiry activities. Journal of Research in Science Teaching, 38(3), 355–385.

    Article  Google Scholar 

  • Elby, A. (2000). What students’ learning of representations tells us about constructivism. Journal of Mathematical Behavior, 19, 481–502.

    Article  Google Scholar 

  • Ford, M. J. (2003). Representing and meaning in history and in classrooms: Developing symbols and conceptual organizations of free-fall motion. Science & Education, 12(1), 1–25.

    Article  Google Scholar 

  • Giere, R. N. (1988). Explaining science: A cognitive approach. Chicago: University of Chicago Press.

    Book  Google Scholar 

  • Guzdial, M. (1995). Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments, 4(1), 1–44.

    Article  Google Scholar 

  • Guzdial, M. (2008). Paving the way for computational thinking. Communications of the ACM: Education Column. 51(8).

    Google Scholar 

  • Halloun, I. A., & Hestenes, D. (1985). The initial knowledge state of college physics students. American Journal of Physics, 53(11), 1043–1056.

    Article  Google Scholar 

  • Hambrusch, S., Hoffmann, C., Korb, J. T., Haugan, M., & Hosking, A. L. (2009). A multidisciplinary approach towards computational thinking for science majors. In Proceedings of the 40th ACM Technical Symposium on Computer Science Education (SIGCSE '09). ACM, New York, NY, USA, 183–187.

  • Hammer, D. (1996). Misconceptions or p-prims: How may alternative perspectives of cognitive structure influence instructional perceptions and intentions? Journal of the Learning Sciences, 5(2), 97–127.

    Google Scholar 

  • Harel, I., & Papert, S. (1991). Software design as a learning environment. Constructionism. Norwood, NJ: Ablex Publishing Corporation. pp. 51–52. ISBN 0-89391-785-0.

  • Hegedus, S. J., & Kaput, J. J. (2004). An Introduction to the Profound Potential of Connected Algebra Activities: Issues of Representation, Engagement, and Pedagogy. Proceedings of the 28th Conference of the International Group for the Psychology of Mathematics Education, 3, 129–136.

    Google Scholar 

  • Ho, C. H. (2001). Some phenomena of problem decomposition strategy for design thinking: Differences between novices and experts. Design Studies, 22(1), 27–45.

    Article  Google Scholar 

  • Hundhausen, C. D., & Brown, J. L. (2007). What You See Is What You Code: A “live” algorithm development and visualization environment for novice learners. Journal of Visual Languages and Computing, 18, 22–47.

    Article  Google Scholar 

  • Jacobson, M., & Wilensky, U. (2006). Complex systems in education: Scientific and educational importance and implications for the learning sciences. Journal of the Learning Sciences, 15(1), 11–34.

    Article  Google Scholar 

  • Kahn, K. (1996). ToonTalk: An animated programming environment for children. Journal of Visual Languages and Computing.

  • Kaput, J. (1994). Democratizing access to calculus: New routes using old routes. In A. Schoenfeld (Ed.), Mathematical thinking and problem solving (pp. 77–156). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Kelleher, C., & Pausch, R. (2005) Lowering the barriers to programming: A taxonomy of programming environments and languages for novice programmers. ACM Computing Surveys, Vol. (37) 83–137.

  • Klahr, D., Dunbar, K., & Fay, A. L. (1990). Designing good experiments to test bad hypotheses. In J. Shrager & P. Langley (Eds.), Computational models of scientific discovery and theory formation (pp. 355–401). San Mateo, CA: Morgan Kaufman.

    Google Scholar 

  • Klopfer, E., Yoon, S., & Um, T. (2005). Teaching complex dynamic systems to young students with StarLogo. The Journal of Computers in Mathematics and Science Teaching, 24(2), 157–178.

    Google Scholar 

  • Kolodner, J. L., Camp, P. J., Crismond, D., Fasse, B., Gray, J., Holbrook, J., Puntambekar, S., & Ryan, M. (2003). Problem-based learning meets case-based reasoning in the middle-school science classroom: Putting learning by design into practice. The Journal of Learning Sciences, 12(4), 495–547.

    Article  Google Scholar 

  • Kramer, J. (2007). Is abstraction the key to computing? Communications of the ACM, 50(4), 36–42. April 2007.

    Article  Google Scholar 

  • Kynigos, C. (2001). E-slate Logo as a basis for constructing microworlds with mathematics teachers (pp. 65–74). Lintz, Austria: Proceedings of the Ninth Eurologo Conference.

    Google Scholar 

  • Kynigos, C. (2007). Using half-baked microworlds to challenge teacher educators’ knowing. Journal of Computers for Math Learning, 12(2), 87–111.

    Article  Google Scholar 

  • Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.

    Article  Google Scholar 

  • Lehrer, R., & Schauble, L. (2006). Cultivating model-based reasoning in science education. In R. K. Sawyer (Ed.), The Cambridge handbook of the learning sciences (pp. 371–388). New York: Cambridge University Press.

    Google Scholar 

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

    Article  Google Scholar 

  • Leinhardt, G., Zaslavsky, O., & Stein, M. M. (1990). Functions, graphs, and graphing: Tasks, learning and teaching. Review of Educational Research, 60, 1–64.

    Google Scholar 

  • Levy, S. T., & Wilensky, U. (2008). Inventing a “mid-level” to make ends meet: Reasoning through the levels of complexity. Cognition and Instruction, 26(1), 1–47.

    Article  Google Scholar 

  • Locke, J. (1690/1979). An essay concerning human understanding. New York: Oxford University Press.

  • Maloney, J., Burd, L., Kafai, Y., Rusk, N., Silverman, B., & Resnick, M. (2004) Scratch: A sneak preview. In Proceedings of Creating, Connecting, and Collaborating through Computing, 104109.

  • McCloskey, M. (1983). Naive theories of motion. In D. Gentner & A. Stevens (Eds.), Mental models (pp. 299–324). Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • National Research Council. (2008). Taking science to school: Learning and teaching science in grades K–8. Washington, DC: National Academy Press.

    Google Scholar 

  • National Research Council. (2010). Report of a workshop on the scope and nature of computational thinking. Washington, DC: The National Academies Press.

    Google Scholar 

  • Nersessian, N. J. (1992). How do scientists think? Capturing the dynamics of conceptual change in science. In R. N. Giere (Ed.), Cognitive models of science (pp. 3–45). MN: University of Minnesota Press. Minneapolis.

    Google Scholar 

  • Oshima, Y. (2005). Kedama: A GUI-based interactive massively parallel particle programming system. Proceedings of the 2005 IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC’05).

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

    Google Scholar 

  • Papert, S. (1991). Situating constructionism. In I. Harel & S. Papert (Eds.), Constructionism. Norwood, NJ: Ablex Publishing Corporation.

    Google Scholar 

  • Penner, D. E., Lehrer, R., & Schauble, L. (1998). From physical models to biomechanics: A design-based modeling approach. Journal of the Learning Sciences, 7(3–4), 429–449.

    Google Scholar 

  • Perkins, D. N., & Simmons, R. (1988). Patterns of misunderstanding: An integrative model for science, math, and programming. Review of Educational Research, 58(3), 303–326.

    Google Scholar 

  • Redish, E. F., & Wilson, J. M. (1993). Student programming in the introductory physics course: M.U.P.P.E.T. American Journal of Physics, 61, 222–232.

    Article  Google Scholar 

  • Reiner, M., Slotta, J. D., Chi, M. T. H., & Resnick, L. B. (2000). Naive physics reasoning: A commitment to substance-based conceptions. Cognition and Instruction, 18(1), 1–34.

    Article  Google Scholar 

  • Repenning, A. (1993). Agentsheets: A tool for building domain-oriented visual programming. Conference on Human Factors in Computing Systems, 142–143.

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

    Google Scholar 

  • Roschelle, J., & Teasley, S. D. (1994). The construction of shared knowledge in collaborative problem solving. NATO ASI Series F Computer and Systems Sciences, 128, 69–69.

    Google Scholar 

  • Roschelle, J., Digiano, C., Pea, R. D., & Kaput, J. (1999). Educational Software Components of Tomorrow (ESCOT), Proceedings of the International Conference on Mathematics/Science Education & Technology (M/SET), March 1–4, 1999. San Antonio, USA.

  • Sandoval, W. A., & Millwood, K. (2005). The quality of students’ use of evidence in written scientific explanations. Cognition and Instruction, 23(1), 23–55.

    Article  Google Scholar 

  • Schauble, L., Klopfer, L. E., & Raghavan, K. (1991). Students’ transition from an engineering model to a science model of experimentation. Journal of Research in Science Teaching, 28, 859–882.

    Article  Google Scholar 

  • Schmidt, D. C. (2006). Guest editor’s introduction: Model-driven engineering. Computer, 39(2), 25–31.

    Article  Google Scholar 

  • Segedy, J. R., Kinnebrew, J. S., & Biswas, G. (2012). Promoting metacognitive learning behaviors using conversational agents in a learning by teaching environment. Educational Technology Research & Development.

  • Sengupta, P. (2011). Design Principles for a Visual Programming Language to Integrate Agent-based modeling in K-12 Science. In: Proceedings of the Eighth International Conference of Complex Systems (ICCS 2011), pp 1636–1637.

  • Sengupta, P., & Farris, A. V. (2012). Learning Kinematics in Elementary Grades Using Agent-based Computational Modeling: A Visual Programming Based Approach. Proceedings of the 11th International Conference on Interaction Design & Children, pp 78–87.

  • 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 

  • Sengupta, P., & Wilensky, U. (2011). Lowering the learning threshold: Multi-agent-based models and learning electricity. In M. S. Khine & I. M. Saleh (Eds.), Dynamic modeling: Cognitive tool for scientific inquiry (pp. 141–171). New York, NY: Springer.

    Google Scholar 

  • Sengupta, P., Farris, A. V., & Wright, M. (2012). From agents to aggregation via aesthetics: Learning mechanics with visual agent-based computational modeling. Technology, Knowledge & Learning, 17(1–2), 23–42.

    Article  Google Scholar 

  • Sherin, B. (2001). A comparison of programming languages and algebraic notation as expressive languages for physics. International Journal of Computers for Mathematics Learning:, 6, 1–61.

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Smith, J. P., diSessa, A. A., & Roschelle, J. (1993). Misconceptions reconceived: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3(2), 115–163.

    Article  Google Scholar 

  • Smith, D., Cypher, A., & Tesler, L. (2000). Programming by example: Novice programming comes of age. Communications of the ACM, 43(3), 75–81.

    Article  Google Scholar 

  • Soloway, E. (1993). Should we teach students to program? Communications of the ACM, 36(10), 21–24.

    Article  Google Scholar 

  • Tan, J., & Biswas, G. (2007). Simulation-based game learning environments: Building and sustaining a fish tank. In Proceedings of the First IEEE International Workshop on Digital Game and Intelligent Toy Enhanced Learning (pp. 73–80). Jhongli, Taiwan.

  • Tanimoto, S. L. (1990). VIVA: A visual language for image processing. Journal of Visual Languages and Computing, 1, 127–139.

    Article  Google Scholar 

  • Von Glaserfeld, E. (1991). Abstraction, re-presentation, and reflection: An interpretation of experience and of Piaget’s approach. In L. P. Steffe (Ed.), Epistemological foundations of mathematical experience (pp. 45–67). New York: Springer.

    Chapter  Google Scholar 

  • White, B. Y., & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. Artificial Intelligence, 42(1), 99–157.

    Article  Google Scholar 

  • Wilensky, U. (1999). NetLogo. Center for Connected Learning and Computer-Based Modeling (http://ccl.northwestern.edu/netlogo). Northwestern University, Evanston, IL.

  • Wilensky, U., & Novak, M. (2010). Understanding evolution as an emergent process: Learning with agent-based models of evolutionary dynamics. In R. S. Taylor & M. Ferrari (Eds.), Epistemology and science education: Understanding the evolution vs. Intelligent design controversy. New York: Routledge.

    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 & Instruction, 24(2), 171–209.

    Article  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).

  • Wing, J. M. (2006) Computational Thinking. Communications of the ACM, vol. 49, no.3 March 2006, pp. 33–35.

  • Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society, 366, 3717–3725.

    Google Scholar 

Download references

Acknowledgments

Thanks to Amanda Dickes, Amy Voss Farris, Gokul Krishnan, Brian Sulcer, Jaymes Winger, and Mason Wright (in no particular order), who helped in developing the system and running the study. Earlier versions of the paper were presented at CSEDU 2012, and ICCE 2012. This work is partially supported by NSF IIS # 1124175 and NSF Early CAREER # 1150230.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pratim Sengupta.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Sengupta, P., Kinnebrew, J.S., Basu, S. et al. Integrating computational thinking with K-12 science education using agent-based computation: A theoretical framework. Educ Inf Technol 18, 351–380 (2013). https://doi.org/10.1007/s10639-012-9240-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10639-012-9240-x

Keywords

Navigation