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

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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).

Keywords

  • Modeling Language
  • Average Speed
  • Primitive Element
  • Content Domain
  • System Dynamic Perspective

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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.

    CrossRef  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 

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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

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

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