Students Becoming Chemists: Developing Representationl Competence

  • Robert Kozma
  • Joel Russell
Part of the Models and Modeling in Science Education book series (MMSE, volume 1)

Abstract

This chapter examines the role that representations and visualizations can play in the chemical curriculum. Two types of curricular goals are examined: students’ acquisition of important chemical concepts and principles and students’ participation in the investigative practices of chemistry—“students becoming chemists.” Literature in learning theory and research support these two goals and this literature is reviewed. The first goal relates to cognitive theory and the way that representations and visualizations can support student understanding of concepts related to molecular entities and processes that are not otherwise available for direct perception. The second goal relates to situative theory and the role that representations and visualizations play in development of representational competence and the social and physical processes of collaboratively constructing an understanding of chemical processes in the laboratory. We analyze research on computer-based molecular modeling, simulations, and animations from these two perspectives and make recommendations for instruction and future research.

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References

  1. American Association for the Advancement of Science, 1993, Benchmarks for science literacy, Oxford University Press, New York.Google Scholar
  2. American Association for the Advancement of Science, 2001, Atlas of science literacy, American Association for the Advancement of Science, Washington, D.C.Google Scholar
  3. Amman, K., and Knorr Cetina, K., 1990, The fixation of (visual) evidence, in: Representation in scientific practice, M. Lynch, and S. Woolgar, eds., MIT Press, Cambridge, MA, pp. 85–122.Google Scholar
  4. Allendoerfer, R., 2002, KinSimXP, a chemical kinetics simulation, Journal of Chemical Education, 79(5): 638–639.Google Scholar
  5. Anderson, W., 1984, Between the library and the laboratory: The language of chemistry in eigthteenth-century France, Johns Hopkins University Press, Baltimore, MD.Google Scholar
  6. Baddeley, A., 1999, Human Memory, Allyn & Bacon, Needham, MA.Google Scholar
  7. Chi, M., Feltovich, P., and Glaser, R., 1981, Categorization and representation of physics problems by experts and novices, Cognitive Science, 5: 121–152.Google Scholar
  8. Cody, J., and Wiser, D., 2003, Laboratory sequence in computational methods for introductory chemistry, Journal of Chemical Education, 80(7): 793–795.Google Scholar
  9. Dabrowiak, J., Hatala, P., and McPike, J., 2000, A molecular modeling program for teaching structural biochemistry, Journal of Chemical Education, 77(3): 397–400.Google Scholar
  10. diSessa, A., Hammer, D., Sherin, B., and Kolpakowski, T., 1991, Inventing graphing: Meta-representational expertise in children, Journal of Mathematical Behavior, 10(2): 117–160.Google Scholar
  11. Dori, Y., and Barak, M., 2001, Virtual and physical molecular modeling: Fostering model perception and spatial understanding, Educational Technology & Society, 4(1): 61–74.Google Scholar
  12. Dori, Y., Barak, M., and Adir, N., 2003, A web-based chemistry course as a means to foster freshmen learning, Journal of Chemical Education, 80(9): 1089–1092.Google Scholar
  13. Dunbar, K., 1997, How scientists really reason: Scientific reasoning in real-world laboratories, in: Thenature of insight, R. Sternberg and J. Davidson, eds., MIT Press, Cambridge, MA, pp. 365–396.Google Scholar
  14. Flemming, S., Hart, G., and Savage, P., 2000, Molecular orbital animations for organic chemistry, Journal of Chemical Education, 77(6): 790–793.Google Scholar
  15. Francoeur, E., 1997, The forgotten tool: The use and development of molecular models, Social Studies of Science, 27: 7–40.Google Scholar
  16. Francoeur, E., 2002, Cyrus Levinthal, the Kluge, and the origins of interactive molecular graphics, Endeavour, 26(4): 127–131.CrossRefGoogle Scholar
  17. Gable, D., 1998, The complexity of chemistry and implications for teaching, in: International handbook of science education, B. Fraser, and K. Tobin, eds., Kluwer Academic Publishers, Dordrecht/Boston/London, pp. 233–248.Google Scholar
  18. Glaser, R., and Chi, M., 1988, Overview, in: The nature of expertise, M. Chi, R. Glaser, and M. Farr, eds., Lawrence Erlbaum Associates, Hillsdale, NJ, pp.xv–xxviii.Google Scholar
  19. Gilbert, J., and Boulter, C., 1998, Learning science through models and modeling, in: International Handbook of Science Education, B. Fraser, and K. Tobin, eds., Kluwer Academic Publishers, Dordrecht/Boston/London, pp. 53–66.Google Scholar
  20. Goodwin, C., 1995, Seeing in depth, Social Studies of Science, 25: 237–274.Google Scholar
  21. Greeno, J., 1998, The situativity of knowing, learning, and research, American Psychologist, 53(1): 5–26.CrossRefGoogle Scholar
  22. Hessley, R., 2000, Computational investigations for undergraduate organic chemistry: predicting the mechanism of the Ritter reaction, Journal of Chemical Education, 77(2): 202.Google Scholar
  23. Hoffman, R., and Laszlo, P., 1991, Representation in Chemistry, Angewandte Chemie, 30(1): 1–16.Google Scholar
  24. Holmes, J., and Gettys, N., 2003, General chemistry collection, 7th Edition abstract of special issue 16, 7th Edition, a CD-ROM for students, Journal of Chemical Education, 80(6): 709–710.Google Scholar
  25. Jones, M., 2001, Molecular modeling in the undergraduate chemistry curriculum, Journal of Chemical Education, 78(7): 867–868.Google Scholar
  26. Johnstone, A., 1997, Chemistry teaching — science or alchemy?, Journal of Chemical Education, 74(3): 262–268.Google Scholar
  27. Kantardjieff, K., Hardinger, S., and Van Willis, W., 1999, Introducing computers early in the undergraduate chemistry curriculum, Journal of Chemical Education, 76(5): 694–697.Google Scholar
  28. Kozma, R., 2000a, Representation and language: The case for representational competence in the chemistry curriculum. Paper presented at the Biennial Conference on Chemical Education, Ann Arbor, MI.Google Scholar
  29. Kozma, R., 2000b, Students collaborating with computer models and physical experiments, in: Proceedings of the Conference on Computer-Supported Collaborative Learning 1999, J. Roschelle, and C. Hoadley, eds., Erlbaum, Mahwah, NJ.Google Scholar
  30. Kozma, R., 2000c, The use of multiple representations and the social construction of understanding in chemistry, in: Innovations in science and mathematics education: Advanced designs for technologies of learning, M. Jacobson, and R. Kozma, eds., Erlbaum, Mahwah, NJ, pp. 11–45.Google Scholar
  31. Kozma, R., 2003, Material and social affordances of multiple representations for science understanding, Learning and Instruction, 13(2): 205–226.CrossRefGoogle Scholar
  32. Kozma, R, Chin, E., Russell, J., and Marx, N., 2000, The role of representations and tools in the chemistry laboratory and their implications for chemistry learning, Journal of the Learning Sciences, 9(2): 105–143.CrossRefGoogle Scholar
  33. Kozma, R., and Russell, J., 1997, Multimedia and Understanding: Expert and Novice Responses to Different Representations of Chemical Phenomena, Journal of Research in Science Teaching, 43(9): 949–968.Google Scholar
  34. Krajcik, J. S., 1991, Developing students’ understanding of chemical concepts, in: The psychology of learning science, S. Glynn, R. Yeany, and B. Britton, eds., Erlbaun, Hillsdale, NJ, pp.117–147.Google Scholar
  35. Krajcik, J., Blumenfeld, P., Marx, R., Bass, K., Fredricks, J., and Soloway, E., 1998, Inquiry in project-based science classrooms: Initial attempts by middle school students, Journal of the Learning Sciences, 7(3&4): 313–351.Google Scholar
  36. Larkin, J., 1983, The role of problem representation in physics, in Mental models, D. Gentner, and A. Stevens, eds., Erlbaum, Hillsdale, NJ, pp. 75–98.Google Scholar
  37. Larkin, J., McDermott, J., Simon, D., and Simon, H., 1980, Expert and novice performance in solving physics problems, Science, 208: 1335–1342.Google Scholar
  38. Lave, J. and Wenger, E., 1991, Situated learning, Cambridge University Press, New York.Google Scholar
  39. Lemke, J., 1990, Talking science: Language, learning, and values, Ablex, Norwood, NJ.Google Scholar
  40. Martin, N., 1998, Integration of computational chemistry into the chemistry curriculum, Journal of Chemical Education, 75(2), 241–243.Google Scholar
  41. Matta, C., and Gillespie, R., 2002, Understanding and interpreting molecular electron density distributions, Journal of Chemical Education, 79(9): 1141–1152.Google Scholar
  42. Mayer, R., 2001, Multimedia Learning, Cambridge University Press, Cambridge, UK.Google Scholar
  43. Mayer, R., 2002, Cognitive theory and the design of multimedia instruction: An example of the two-way street between cognition and instruction, New Directions for Teaching and Learning, 89: 55–71.Google Scholar
  44. Mayer, R., 2003, The promise of multimedia learning: Using the same instructional design methods across different media, Learning and Instruction, 13(2): 125–139.CrossRefGoogle Scholar
  45. Michalchik, V., Rosenquist, A., Kozma, R., Schank, P., and Coppola, B., 2004, Representational resources for constructing shared understandings in the high school chemistry classroom [technical report], SRI International, Menlo Park, CA.Google Scholar
  46. Montgomery, C., 2001, Integrating molecular modeling into the inorganic chemistry laboratory, Journal of Chemical Education, 78(6): 840–844.Google Scholar
  47. Nakhleh, M. B., 1992, Why some students don’t learn chemistry: Chemical misconceptions, Journal of Chemical Education, 69: 191–196.Google Scholar
  48. National Research Council, 1996a, The National Science Education Standards, National Academy Press, Washington, D.C.Google Scholar
  49. National Research Council, 1996b, From analysis to action: Undergraduate education in science, mathematics, engineering, and technology, National Academy Press, Washington, D.C.Google Scholar
  50. National Science Foundation, 1996, Shaping the future: New expectations for undergraduate education in science, mathematics, engineering, and technology, National Science Foundation, Washington, DC.Google Scholar
  51. O’Neill, D., and Polman, J., 2004, Why educate “little scientists?”: Examining the potential of practicebased scientific literacy, Journal of Research in Science Teaching, 41(3): 234–266.Google Scholar
  52. Paiva, J, Gil, V., and Correia, C., 2002, LeChat: Simulation in chemical equilibrium, Journal of Chemical Education, 79(5): 640.Google Scholar
  53. Paivio, A., 1986, Mental Representations: A Dual Coding Approach, Oxford University Press, New York.Google Scholar
  54. Papadopoulos, N. and Limniou, 2003, pH Titration Simulator, Journal of Chemical Education, 80(9): 709–710.Google Scholar
  55. Paselk, R., and Zoellner, R., 2002, Molecular modeling and computational chemistry at Humboldt State University, Journal of Chemical Education, 79(10): 1192–1194.Google Scholar
  56. Piaget, J., 1972, The psychology of the child, Basic Books, New York.Google Scholar
  57. Robinson, W., 2000, A view of the science education research literature: Scientific discovery learning with computer simulations, Journal of Chemical Education, 77(1): 17–18.Google Scholar
  58. Robinson, W., 2004, Cognitive theory and the design of multimedia instruction, Journal of Chemical Education, 81(1): 10–13.Google Scholar
  59. Roth, W-M., and Bowen, G., 1999, Complexities of graphical representations during lectures: A phenomenological approach, Learning and Instruction, 9: 235–255.CrossRefGoogle Scholar
  60. Roth, W-M., and McGinn, M., 1998, Inscriptions: a social practice approach to representations, Review of Educational Research, 68: 35–59.Google Scholar
  61. Russell, J., and Kozma, R., 1994, 4M:Chem — multimedia and mental models in chemistry, Journal of Chemical Education, 71(8) 669–670.Google Scholar
  62. Sanger, M., and Greenbowe, T., 2000, Addressing student misconceptions concerning electron flow in aqueous solutions with instruction including computer animations and conceptual change strategies, International Journal of Science Education, 22(5): 521–537.Google Scholar
  63. Sanger, M., Phelps, A., and Fienhold, J., 2000, Using a computer animation to improve students’ conceptual understanding of a can-crushing demonstration, Journal of Chemical Education, 77(11): 517–1520.Google Scholar
  64. Schoenfeld-Tacher, R., 2000, Relation of Student Characteristics to Learning of Basic Biochemistry Concepts from a Multimedia Goal-Based Scenario, PhD dissertation, University of Northern Colorado.Google Scholar
  65. Schoenfeld-Tacher, R., Jones. L., and Persichitte, K., 2001, Differential effects of a multimedia goal-based scenario to teach introductory biochemistry — who benefits most?, Journal of Science Education and Technology, 10(4): 305–317.CrossRefGoogle Scholar
  66. Schank, P. and Kozma, R., 2002, Learning chemistry through the use of a representation-based knowledge-building environment, Journal of Computers in Mathematics and Science Teaching, 21(3): 253–279.Google Scholar
  67. Schank, R., Fano, A., Bell, B., and Jona, M., 1993, The design of goal-based scenarios, Journal of the Learning Sciences, 3, 305–345.Google Scholar
  68. Shusterman, G., and Shusterman, A., 1997, Teaching chemistry with electron density models, Journal of Chemical Education, 74(7): 771–776.Google Scholar
  69. Williamson, V., 1992, The effects of computer animation emphasizing the particulate nature of matter on the understandings and misconceptions of college general chemistry student, Unpublished doctoral dissertation, University of Oklahoma.Google Scholar
  70. Williamson, V., and Abraham, M., 1995, The effects of computer animation on the particulate mental models of college chemistry students, Journal of Research in Science Teaching, 32(5): 521–534.Google Scholar
  71. Woolgar, S., 1990, Time and documents in researcher interaction: Some ways of making out what is happening in experimental science, in: Representation in scientific practice, M. Lynch, and S. Woolgar, eds., MIT Press, Cambridge, MA, pp. 123–152.Google Scholar
  72. Yang, E., Greenbowe, T., and Andre, T., 2004, The effective use of interactive software program to reduce students’ misconceptions about batteries, Journal of Chemical Education, 81(4): 587–595.Google Scholar
  73. Vogel, G. (1996). Science education: Global review faults U.S. curricula, Science, 274, 335.Google Scholar
  74. Vygotsky, L., 1986, Thought and language, MIT Press, Boston.Google Scholar
  75. Vygotsky, L., and Vygotsky, S., 1980. Mind in society: The development of higher psychological processes, Harvard University Press, Cambridge, MA.Google Scholar
  76. Zare, R., 2002, Visualizing Chemistry, Journal of Chemical Education, 79(11): 1290–1291.Google Scholar

Copyright information

© Springer 2005

Authors and Affiliations

  • Robert Kozma
    • 1
  • Joel Russell
    • 2
  1. 1.Center for Technology in LearningSRI InternationalMenlo Park
  2. 2.Department of ChemistryOakland UniversityRochesterUSA

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