Thinking like a wolf, a sheep, or a firefly: Learning biology through constructing and testing computational theories - An embodied modeling approach

Cognition and Instruction

Volumn 24, Issue 2, 2006, Pages 171-209

  Wilensky, Uri ^a   Reisman, Kenneth ^b  

^a NORTHWESTERN UNIVERSITY   (United States)

^b STANFORD UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

EID: 33744973542     PISSN: 07370008     EISSN: None     Source Type: Journal    
DOI: 10.1207/s1532690xci2402_1     Document Type: Review

References (78)

1. Abelson, H., & diSessa, A, (1981). Turtle geometry: The computer as a medium for exploring mathematics. Cambridge, MA: MIT Press.
2. Allen, G. (1975). Life science in the twentieth century. New York: Wiley.
3. Beer, R. D. (1990). Intelligence as adaptive behavior: An experiment in computational neuroethology. Cambridge, MA: Academic.
4. Buck, J. (1988). Synchronous rhythmic flashing of fireflies II. Quarterly Review of Biology, 63, 265-287.
5. Buck, J., & Buck, E. (1968, March 22). Mechanism of rhythmic synchronous flashing of fireflies. Science, 159, 1319-1327.
6. Buck, J., & Buck, E. (1976, May). Synchronous fireflies. Scientific American, 234, 74-85.
7. Buneman, O., Barnes, C., Green, J., & Nielson, D. (1980). Principles and capabilities of 3-E-M particle simulations. Journal of Computational Physics, 39, 1-44.
8. Carey, S. (1986). Cognitive science and science education. American Psychologist, 41, 1123-1130.
9. Carlson, A. D., & Copeland, J. (1985). Flash communication in fireflies. Quarterly Review of Biology, 60, 415-436.
10. Chen, D. (1999). Confronting complexity: A new meaning to world literacy. In E. Chaisson & T. C. Kim (Eds.), The thirteenth labor: Improving science education (pp. 87-100). Amsterdam: Gordon & Breach.
11. Dennett, D. (1995). Darwin's dangerous idea: Evolution and the meanings of life. New York: Simon & Schuster.
12. Elton, C. (1966). Animal ecology. New York: October House.
13. Epstein, J., & Axtell, R. (1996). Growing artificial societies: Social science from the bottom up. Washington, DC: Brookings Institute.
14. Feurzeig, W. (1984). The Logo lineage. In S. Ditlea (Ed.), Digital deli (pp. 158-161). New York: Workman.
15. Feurzeig, W., & Roberts, N. (1999). Modeling and simulations in science and mathematics education. New York: Springer-Verlag.
16. Forrest, S. (1989). Emergent computation: Self-organizing, collective, and cooperative phenomena in natural and competing networks. Amsterdam: North-Holland.
17. Cause, G. F. (1934). The struggle for existence. Baltimore: Williams & Wilkins.
18. Gobert, J., Horwitz, P., Tinker, B., Buckley, B., Wilensky, U., Levy, S., et al. (2004). Modeling across the curriculum: Scaling up modeling using technology. In R. Alterman & D. Kirsh (Eds.), Proceedings of the Twenty-fifth Annual Meeting of the Cognitive Science Society (p. 1349). Boston: Cognitive Science Society.
19. Hammer, D. (1994). Epistemological beliefs in introductory physics. Cognition and Instruction, 12, 151-183.
20. Hatano, G., & Inagaki, K. (1987). Everyday biology and school biology: How do they interact? The Quarterly Newsletter of Comparative Human Cognition, 9, 120-128.
21. Hudson, G. (1918, December 6). Concerted flashing of fireflies. Science, 48, 573-575.
22. Huffaker, C. (1958). Experimental studies on predation: Dispersion factors and predator-prey oscillations. Hilgardia, 27, 343-383.
23. Huston, M., DeAngelis, D., & Post, W. (1988). New computer models unify ecological theory. BioScience, 38, 682-691.
24. Jackson, S., Stratford, S., Krajcik, J., & Soloway, E. (1996). A learner-centered tool for students building models. Communications of the ACM, 39, 48-49.
25. Jacobson, M., Brecher, K., Clemens, M., Farrell, W., Kaput, J., Reisman, K., et al. (1998). Education in complex systems. In Y. Bar-Yam (Ed.), Proceedings of the Second International Conference on Complex Systems (pp. 549-556). Nashua, NH: New England Complex Systems Institute.
26. Jacobson, M., & Kozma, R. (2000). Innovations in science and mathematics education: Advanced designs for technologies of learning. Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
27. Judson, O. (1994). The rise of the individual-based model in ecology. TREE, 9(1), 9-14.
28. Jungck, J. R., & Calley, J. (1985). Strategic simulations and post-Socratic pedagogy: Constructing computer software to develop long-term inference through experimental inquiry. American Biology Teacher, 47, 11-15.
29. Kauffman, S. (1995). At home in the universe: The search for the laws of self-organization and complexity. Oxford, England: Oxford University Press.
30. Keeling, M., & Gilligan, C. (2000, October 19). Metapopulation dynamics of bubonic plague. Nature, 407, 903-906
31. Keen, R., & Spain, J. (1992). Computer simulation in biology. New York: Wiley-Liss.
32. Kuhn, D. (1993). Science as argument: Implications for teaching and learning scientific thinking. Science Education, 77, 319-337.
33. Langton, C. (1994). Artificial life III. Reading, MA: Addison-Wesley.
34. Lehrer, R., & Schauble, L. (2000). Modeling in mathematics and science. In R. Glaser (Ed.), Advances in instructional psychology: Vol. 5. Educational design and cognitive science (pp. 101-159). Mahwah, NJ: Lawrence Erlbaum Associates, Inc.
35. Lotka, A. J. (1925). Elements of physical biology. New York: Dover.
36. Luckinbill, L. (1973). Coexistence in laboratory populations of Paramecium aurelia and its predator Didnium nasutum. Ecology, 54, 1320-1327.
37. Macs, P. (1990). Designing autonomous agents, theory and practice from biology to engineering and back. Cambridge, MA: MIT Press.
38. Marion G., Renshaw E., & Gibson G. (2000). Stochastic modeling of environmental variation for biological populations. Theoretical Population Biology, 57, 197-217.
39. Mirollo, R., & Strogatz, S. (1990). Synchronization of pulse-coupled biological oscillators. SIAM Journal of Applied Mathematics, 50, 1645-1662.
40. Morse, E. (1916, February 14). Fireflies flashing in unison. Science, 43, 169-170.
41. Murname, R., & Levy, F. (1996). Teaching the new basic skills: Principles for educating children to thrive in a changing economy. New York: Free Press.
42. Murray, J. D. (1989). Mathematical biology. Berlin, Germany: Springer.
43. Noss, R. (1988). The computer as a cultural influence in mathematical learning. Educational Studies in Mathematics, 19, 251-268.
44. Noss, R., & Hoyles, C. (1996). Windows on mathematical meanings: Learning cultures and computers. Dordrecht, The Netherlands: Kluwer.
45. Ogborn, J. (1999). Modeling clay for thinking and learning. In N. Roberts, W. Feurzeig, & B. Hunter (Eds.), Computer modeling and simulation in science education (pp. 5-37). Berlin, Germany: Springer-Verlag.
46. Papert, S. (1972). Teaching children to be mathematicians vs. teaching about mathematics. International Journal of Mathematics Education in Science and Technology, 3, 249-262.
47. Papert, S. (1980). Mindstorms: Children, computers, and powerful ideas. New York: Basic Books.
48. Popper, K. (1959). The logic of scientific discovery. London: Hutchinson.
49. Purves, W., Orians, G., & Heller, H. (1992). Life: The science of biology (3rd ed.). Sunderland, MA: Sinauer Associates.
50. Quine, W. V. (1960). Word and object. Cambridge, MA: MIT Press.
51. Reisman, K. (1998). Distributed computer-based modeling. Unpublished undergraduate honors thesis, Tufts University, Boston.
52. Repenning, A. (1993). AgentSheets: A tool for building domain-oriented dynamic, visual environments. Unpublished doctoral dissertation, University of Colorado, Boulder.
53. Repenning, A. (1994). Programming substrates to create interactive learning environments. Interactive learning environments, 4, 45-74.
54. Resnick, M. (1994). Turtles, termites and traffic jams: Explorations in massively parallel microworlds. Cambridge, MA: MIT Press.
55. Resnick, M. (1996). Beyond the centralized mindset. Journal of the Learning Sciences, 5, 1-22.
56. Resnick, M., & Wilensky, U. (1993, April). Beyond the deterministic, centralized mindsets: New thinking for new sciences. Paper presented at the meeting of the American Educational Research Association, Atlanta, GA.
57. Resnick, M., & Wilensky, U. (1998). Diving into complexity: Developing probabilistic decentralized thinking through role-playing activities. Journal of the Learning Sciences, 7, 153-171.
58. Roberts, N., & Barclay, T. (1988, Fall). Teaching model building to high school students: Theory and reality. Journal of Computers in Mathematics and Science Teaching, 8(1), 13-24.
59. Rosenzweig, M. (1971, January 29). Paradox of enrichment: Destabilization of exploitation ecosystems in ecological time. Science, 171, 385-387.
60. Salk, J. (1983). Anatomy of reality: Merging of intuition and reason. New York: Columbia University Press.
61. Smith, D. C., Cypher, A., & Spohrer, J. (1994). Kidsim: Programming agents without a programming language. Communications of the ACM, 37, 55-67.
62. Smith, J. P., diSessa, A. A., & Roschelle, J. (1994). Reconceiving misconceptions: A constructivist analysis of knowledge in transition. Journal of the Learning Sciences, 3, 115-163.
63. Stroup, W. (1996). Embodying a nominalist constructivism: Making graphical sense of learning the calculus of how much and how fast. Unpublished doctoral dissertation, Harvard University, Cambridge, MA.
64. Taylor, C., Jefferson, D., Turner, S., & Goldman, S. (1989). RAM: Artificial life for the exploration of complex biological systems. In C. Langton (Ed.), Artificial life (pp. 275-295). Reading, MA: Addison-Wesley.
65. Tisue, S., & Wilensky, U. (2004, May). NetLogo: A simple environment for modeling complexity. Paper presented at the Fifth Proceedings of the International Conference on Complex Systems, Boston.
66. Volterra, V. (1926, October 16). Fluctuations in the abundance of a species considered mathematically. Nature, 118, 558-560.
67. White, B., & Horwitz, P. (1988). Computer microworlds and conceptual change: A new approach to science education. In P. Ramsden (Ed.), Improving learning: New perspectives (pp. 69-80). London: Kogan Page.
68. Wilensky, U. (1993). Connected mathematics: Building concrete relationships with mathematical knowledge. Unpublished doctoral dissertation, Massachusetts Institute of Technology, Cambridge.
69. Wilensky, U. (1995). Learning probability through building computational models. In Proceedings of the Nineteenth International Conference on the Psychology of Mathematics Education (pp. 152-159). Recife, Brazil: Program Committee of the 19th PME conference.
70. Wilensky, U. (1996). Modeling rugby: Kick first, generalize later? International Journal of Computers for Mathematical Learning, 1, 125-131.
71. Wilensky, U. (1997a). StarLogoT 2002 [Computer software]. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling.
72. Wilensky, U. (1997b). What is normal anyway? Therapy for epistemological anxiety. Educational Studies in Mathematics, 33, 171-202.
73. Wilensky, U. (1998). Connected models [Computer software]. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling.
74. Wilensky, U. (1999a). GasLab - An extensible modeling toolkit for exploring micro- and macro- views of gases. In N. Roberts, W. Feurzeig, & B. Hunter (Eds.), Computer modeling and simulation in science education (pp. 151-178). Berlin, Germany: Springer-Verlag.
75. Wilensky, U. (1999b). NetLogo (Version 3.0.2) [Computer software]. Evanston, IL: Northwestern University, Center for Connected Learning and Computer-Based Modeling.
76. Wilensky, U. (2001, August). Modeling nature's emergent patterns with multi-agent languages. Paper presented at Eurologo 2001, Linz, Austria.
77. Wilensky, U., & Resnick, M. (1995, April). New thinking for new sciences: Constructionist approaches for exploring complexity. Paper presented at the meeting of the American Educational Research Association, San Francisco.
78. 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, 3-19.