Skip to main content

GasLab—an Extensible Modeling Toolkit for Connecting Micro-and Macro-properties of Gases

  • Chapter
Modeling and Simulation in Science and Mathematics Education

Part of the book series: Modeling Dynamic Systems ((MDS))

Abstract

Computer-based modeling tools have largely grown out of the need to describe, analyze, and display the behavior of dynamic systems. Recent decades have seen increasing recognition of the importance of understanding the behavior of dynamic systems—how systems of many interacting elements change and evolve over time and how global phenomena can arise from local interactions of these elements. New research projects on chaos, self-organization, adaptive systems, nonlinear dynamics, and artificial life are all part of this growing interest in system dynamics. The interest has spread from the scientific community to popular culture, with the publication of general-interest books about research into dynamic systems (Gleick 1987; Waldrop, 1992; GellMann, 1994; Kelly, 1994; Roetzheim, 1994; Holland, 1995; Kauffman, 1995).

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  • Buldyrev, S.V., Erickson, M.J., Garik, P., Shore, L. S., Stanley, H. E., Taylor, E. F., Trunfio, P.A. & Hickman, P. 1994. Science research in the classroom: The Physics Teacher, 32, 411–415.

    Article  Google Scholar 

  • Chen, D., & Stroup, W. 1993. General systems theory: Toward a conceptual framework for science and technology education for all. Journal for Science Education and Technology, 2(3), 447–459.

    Article  Google Scholar 

  • Cutnell, J., & Johnson, K. 1995. Physics. New York: Wiley.

    Google Scholar 

  • Daston, L. 1987. Rational individuals versus laws of society: From probability to statistics. In Kruger, Daston, L., & Heidelberger, M. (eds.), The probabilistic revolution, vol. 1. Cambridge, MA: M.I.T. Press.

    Google Scholar 

  • Dawkins, R 1976. The selfish gene. Oxford, England: Oxford University Press.

    Google Scholar 

  • Dennett, D. 1995. Darwin’s dangerous idea: Evolution and the meanings of life. New York: Simon & Schuster.

    Google Scholar 

  • diSessa, A. 1986. Artificial worlds and real experience. Instructional Science, 207–227.

    Google Scholar 

  • Doerr, H. 1996. STELLA®: Ten years later: A review of the literature. International Journal of Computers for Mathematical Learning, 1(2), 201–224.

    Article  Google Scholar 

  • Eisenberg, M. 1991. Programmable applications: Interpreter meets interface. MIT AI Memo 1325. Cambridge, MA: AI Lab, M.I.T.

    Google Scholar 

  • Feurzeig, W. 1989. A visual programming environment for mathematics education. Paper presented 4th International Conference for Logo and Mathematics Education. Jerusalem, Israel, August 15.

    Google Scholar 

  • Forrester, J.W. 1968. Principles of systems. Norwalk, CT: Productivity Press.

    Google Scholar 

  • Gell-Mann, M. 1994. The quark and the jaguar. New York: W.H. Freeman.

    MATH  Google Scholar 

  • Giancoli, D. 1984. General physics. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Gigerenzer, G. 1987. Probabilistic thinking and the fight against subjectivity. In Kruger, L., Daston, L., & Heidelberger, M. (eds.), The probabilistic revolution, vol 2 Cambridge, MA: M.I.T. Press.

    Google Scholar 

  • Ginsburg, H., & Opper, S. 1969. Piaget’s theory of intellectual development. Englewood Cliffs, NJ: Prentice-Hall.

    Google Scholar 

  • Giodan, A. 1991. The importance of modeling in the teaching and popularization of science. Trends in Science Education, 41(4).

    Google Scholar 

  • Gleick, J. 1987. Chaos. New York: Viking Penguin.

    MATH  Google Scholar 

  • Hofstadter, D. (1979). Godel, Escher, Bach: An eternal golden braid. New York: Basic Books.

    Google Scholar 

  • Holland, J. 1995. Hidden order: How adaptation builds complexity. Reading, MA: Helix Books/Addison-Wesley.

    Google Scholar 

  • Horwitz, P. 1989. ThinkerTools: Implications for science teaching. In Ellis, J.D. (ed.), 1988 AETS yearbook: Information technology and science education, pp. 59–71.

    Google Scholar 

  • Horwitz, P., Neumann, E, & Schwartz, J. 1994. The Genscope Project. Connections, Spring, 10–11.

    Google Scholar 

  • Jackson, S., Stratford, S., Krajcik, J., & Soloway, E. 1996. A learner-centered tool for students building models. Communications of the ACM, 39(4), 48–49.

    Article  Google Scholar 

  • Kauffman, S. 1995. At home in the universe: The search for the laws of self-organization and complexity. Oxford, England: Oxford University Press

    Google Scholar 

  • Kay, A. C. 1991. Computers, networks and education. Scientific American, September, 138–148.

    Google Scholar 

  • Kelly, K. 1994. Out of control. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Kruger, L., Daston, L., & Heidelberger, M. (eds.) 1987.The probabilistic revolution vol. 1. Cambridge, MA: M.I.T. Press.

    Google Scholar 

  • Langton, C., & Burkhardt, G. 1997. Swarm. Santa Fe, NM: Santa Fe Institute.

    Google Scholar 

  • Lotka, A.J. 1925. Elements of physical biology. New York: Dover Publications.

    MATH  Google Scholar 

  • Mandinach, E.B., & Cline, H.F. 1994. Classroom dynamics: Implementing a technol-ogy-based learning environment. Hillsdale, NJ: Lawrence Erlbaum.

    Google Scholar 

  • Mellar et al. (1994). Learning with artificial worlds: Computer based modelling in the curriculum. London: Falmer Press.

    Google Scholar 

  • Minar, N., Burkhardt, G., Langton, C., & Askenazi, M. 1997. The Swarm simulation system: A toolkit for building multi-agent simulations. http://www.santafe.edu/ projects/swarm/.

    Google Scholar 

  • Minsky, M. 1987. The society of mind. Simon & Schuster Inc., New York.

    Google Scholar 

  • Nemirovsky, R. 1994. On ways of symbolizing: Tthe case of Laura and the velocity sign.Journal of Mathematical Behavior, 14(4), 389–422.

    Article  Google Scholar 

  • Neumann, E., Feurzeig, W., Garik, P., & Horwitz, P. 1997. OOTL. Paper presented at the European Logo Conference. Budapest: Hungary, 20–23 August.

    Google Scholar 

  • Noss, R., & Hoyles, C. 1996. The visibility of meanings: Modelling the mathematics of banking. International Journal of Computers for Mathematical Learning, 1(1), 3–31.

    Google Scholar 

  • Ogborn, J. 1984. A microcomputer dynamic modelling system.Physics Education 19(3),138–142.

    Article  Google Scholar 

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

    Google Scholar 

  • Papert, S. 1991. Situating constructionism. In Harel, I., & Papert, S. (eds.) Constructionism pp. 1-12. Norwood, NJ: Ablex Publishing.

    Google Scholar 

  • Papert, S. 1996. An exploration in the space of mathematics education.International Journal of Computers for Mathematical Learning.,1(1),95–123.

    Google Scholar 

  • Pea, R. 1985. Beyond amplification: Using the computer to reorganize mental functioning. Educational Psychologist, 20(4), 167–182.

    Article  Google Scholar 

  • Prigogine, I., & Stengers, I. 1984. Order out of chaos: Man’s new dialogue with nature. New York: Bantam Books.

    Google Scholar 

  • Repenning, A. 1993. AgentSheets: A tool for building domain-oriented dynamic, visual environments. Ph.D. dissertation, University of Colorado.

    Google Scholar 

  • Repenning, A. 1994. Programming substrates to create interactive learning environments. Interactive Learning Environments, 4(1), 45–74.

    Article  Google Scholar 

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

    Google Scholar 

  • Resnick, M., & Wilensky, U. 1995. New thinking for new Sciences: Constructionist approaches for exploring complexity. Presented at the annual conference of the American Educational Research Association, San Francisco, CA.

    Google Scholar 

  • Resnick, M., & Wilensky, U. 1998. Diving into Complexity: Developing probabilistic decentralized thinking through role-playing activities. Journal of the Learning Sciences, 7(2), 153–171.

    Article  Google Scholar 

  • Richmond, B., & Peterson, S. 1990. Stella II. Hanover, NH: High Performance Systems.

    Google Scholar 

  • Roberts, N. 1978. Teaching dynamic feedback systems thinking: An elementary view. Management Science, 24(8), 836–843.

    Article  Google Scholar 

  • Roberts, N. 1981. Introducing computer simulation into the high schools: An applied mathematics curriculum. Mathematics Teacher, 74(8), 647–652.

    Google Scholar 

  • Roberts, N., Anderson, D., Deal, R., Garet, M., Shaffer, W. 1983. Introduction to computer simulations: A systems dynamics modeling approach. Reading, MA: Addison-Wesley.

    Google Scholar 

  • Roberts, N., & Barclay, T. 1988. Teaching model building to high school students: Theory and reality.Journal of Computers in Mathematics and Science Teaching, Fall, 13–24.

    Google Scholar 

  • Roetzheim, W. 1994. Entering the complexity lab. Indianapolis, IN: SAMS Publishing.

    Google Scholar 

  • Shore, L. S., Erickson, M. J., Garik, P., Hickman, P., Stanley, H. E., Taylor, E. F., and Trunfio, P. 1992. Learning fractals by “doing science”: Applying cognitive apprenticeship strategies to curriculum design and instruction. Interactive Learning Environments, 2, 205–226.

    Article  Google Scholar 

  • Smith, D. C., Cypher, A., & Spohrer, J. 1994. Kidsim: Programming agents without a programming language. Communications of the ACM, 37(7), 55–67.

    Google Scholar 

  • Starr, P. 1994. Seductions of Sim. The American Prospect, 17, 19–29.

    Google Scholar 

  • Thornton, R., & Sokoloff, D. 1990. Learning motion concepts using real-time microcomputer-based laboratory tools. American Journal of Physics, 58, 9.

    Article  Google Scholar 

  • Tipler, P. 1992. Elementary modern physics. New York: Worth.

    Google Scholar 

  • Tversky, A., & Kahneman, D. 1974. Judgment under uncertainty: Heuristics and biases. Science, 185, 1124–1131.

    Article  Google Scholar 

  • Waldrop, M. 1992. Complexity: The emerging order at the edge of order and chaos. New York: Simon & Schuster.

    Google Scholar 

  • White, B., & Frederiksen, J. 1998. Inquiry, modeling, and metacognition: Making science accessible to all students. Cognition and Instruction, 16(1), 3–118.

    Article  Google Scholar 

  • Wilensky, U. 1991. Abstract meditations on the concrete and concrete implications for mathematics education. In Harel, I., & Papert, P. (eds.), Constructionism. Norwood, NJ: Ablex Publishing, 193–204.

    Google Scholar 

  • Wilensky, U. 1993. Connected mathematics: Building concrete relationships with mathematical knowledge. Ph.D. dissertation, M.I.T.

    Google Scholar 

  • Wilensky, U. 1995a. Paradox, programming and learning probability: A case study in a connected mathematics framework. Journal of Mathematical Behavior, 14(2), 253–280.

    Article  Google Scholar 

  • Wilensky, U. 1995b. Learning probability through building computational models. Proceedings of the Nineteenth International Conference on the Psychology of Mathematics Education. Recife, Brazil, July.

    Google Scholar 

  • Wilensky, U. 1996. Modeling rugby: Kick first, generalize later? International Journal of Computers for Mathematical Learning, 1(1), 125–131.

    Google Scholar 

  • Wilensky, U. 1997. What is normal anyway? Therapy for epistemological anxiety. Educational Studies in Mathematics. Special Edition on Computational Environments in Mathematics Education, ed. R. Noss, (Ed.) 33(2), 171–202.

    Google Scholar 

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

    Google Scholar 

  • Wright, W. 1992a. SimCity. Orinda, CA: Maxis

    Google Scholar 

  • Wright, W. 1992b. SimEarth. Orinda, CA: Maxis

    Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1999 Springer Science+Business Media New York

About this chapter

Cite this chapter

Wilensky, U. (1999). GasLab—an Extensible Modeling Toolkit for Connecting Micro-and Macro-properties of Gases. In: Feurzeig, W., Roberts, N. (eds) Modeling and Simulation in Science and Mathematics Education. Modeling Dynamic Systems. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-1414-4_7

Download citation

  • DOI: https://doi.org/10.1007/978-1-4612-1414-4_7

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4612-7135-2

  • Online ISBN: 978-1-4612-1414-4

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics