A Computational Thinking Approach to Learning Middle School Science

  • Satabdi Basu
  • Gautam Biswas
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7926)

Abstract

Computational Thinking (CT) defines a domain-general, analytic approach to problem solving, combining computer science concepts with practices central to modeling and reasoning in STEM (Science, Technology, Engineering and Mathematics) domains. In our research, we exploit this synergy to develop CTSiM (Computational Thinking in Simulation and Modeling) - a cross-domain, visual programming and agent based, scaffolded environment for learning CT and science concepts simultaneously. CTSiM allows students to conceptualize and build computational models of scientific phenomena, execute the models as simulations, conduct experiments to verify the simulation behaviors against ‘expert behavior’, and use the models to solve real world problems.

Keywords

Computational Thinking Science education Visual Programming Agent-based modeling and simulation Learning by design Scaffolding 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Basu, S., Dickes, A., Kinnebrew, J.S., Sengupta, P., Biswas, G.: CTSiM: A Computational Thinking Environment for Learning Science through Simulation and Modeling. In: Proceedings of the 5th International Conference on Computer Supported Education, Aachen, Germany, pp. 369–378 (2013)Google Scholar
  2. 2.
    Guzdial, M.: Software-realized scaffolding to facilitate programming for science learning. Interactive Learning Environments 4(1), 1–44 (1995)CrossRefGoogle Scholar
  3. 3.
    Hambrusch, S., Hoffmann, C., Korb, J.T., Haugan, M., Hosking, A.L.: A multidisciplinary approach towards computational thinking for science majors. In: Proceedings of the 40th ACM Technical Symposium on Computer Science Education (SIGCSE 2009), pp. 183–187. ACM, New York (2009)CrossRefGoogle Scholar
  4. 4.
    Lehrer, R., Schauble, L.: Cultivating model-based reasoning in science education. In: Sawyer, R.K. (ed.) The Cambridge Handbook of the Learning Sciences, pp. 371–388. Cambridge University Press, New York (2006)Google Scholar
  5. 5.
    National Research Council, A framework for K-12 Science Education: Practices, Crosscutting Concepts, and Core Ideas. The National Academies Press, Washington, DC (2011)Google Scholar
  6. 6.
    Perkins, D.N., Simmons, R.: Patterns of misunderstanding: An integrative model for science, math, and programming. Review of Educational Research 58(3), 303–326 (1988)CrossRefGoogle Scholar
  7. 7.
    Puntambekar, S., Hübscher, R.: Tools for scaffolding students in a complex learning environment: What have we gained and what have we missed? Educational Psychologist 40, 1–12 (2005)CrossRefGoogle Scholar
  8. 8.
    Sengupta, P., Kinnebrew, J.S., Basu, S., Biswas, G., Clark, D.: Integrating Computational Thinking with K-12 Science Education Using Agent-based Computation: A Theoretical Framework. Education and Information Technologies 18(2), 351–380 (2013)CrossRefGoogle Scholar
  9. 9.
    Wing, J.M.: Computational Thinking: What and Why? Link Magazine (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Satabdi Basu
    • 1
  • Gautam Biswas
    • 1
  1. 1.Institute of Software Integrated SystemsVanderbilt UniversityNashvilleU.S.A.

Personalised recommendations