Cognitive Economy in Physics Reasoning: Implications for Designing Instructional Materials

  • Eileen Scanlon
  • Tim O’Shea
Part of the Cognitive Science book series (COGNITIVE SCIEN)


In this chapter we discuss observations arising from our work with think-aloud protocols of novices’ attempts at kinematics problems. We report on some general observations made on their problem-solving techniques and examine one key issue in detail, namely whether multiple representations are a help or hindrance to problem solving. Modeling the successful and unsuccessful problem-solving behavior of pairs of students has led us to formulate the hypothesis of cognitive economy. Novices solving a physics problem can more easily achieve success when they restrict themselves to using only one representation of the problem. The cognitive economy hypothesis has consequences for the design of intelligent tutoring systems (ITS). In particular it implies that imposing constraints on the learning environment, rather than being a wholesale limitation of conventional intelligent tutors, is a positively helpful act.


Production Rule Physic Reasoning Multiple Representation Intelligent Tutoring System Graph Interpretation 
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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  1. Abbot, A. (1964). Ordinary level physics. Oxford: Oxford University Press.Google Scholar
  2. Berry, J., & O’Shea, T. (1984). Mathematical modelling at a distance. Distance Education, 5,(2): 163–173.CrossRefGoogle Scholar
  3. Brown, J. S., & Burton, R. B. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.CrossRefGoogle Scholar
  4. Brown, J. S., & de Kleer, J. (1984). The origin, form and logic of qualitative physical laws. Proceedings of the eighth international joint conference on artificial intelligence (Vol. 2, 1158–69). Los Altos, CA: William Kaufmann.Google Scholar
  5. Bundy, A., Byrd, L., Luger, G., Mellish, C., Milne, R., & Palmer, M. (1979). Mecho: A program to solve mechanics problems (Working Paper 50). Edinburgh: University of Edinburgh, Department of Artificial Intelligence.Google Scholar
  6. Chi, M., Glaser, R., & Rees, E. (1982). Expertise in problem solving. In R. J. Sternberg (Ed.), Advances in the psychology of human Intelligence (Vol. 1, pp. 7–76). Hillsdale, NJ: Lawrence Erlbaum Assoc.Google Scholar
  7. di Sessa, A. (1980). Understanding Aristotelian physics: A study of knowledge-based learning (D.R.S.E. Internal Report). Cambridge, MA: M.I.T.Google Scholar
  8. Evertsz, R. (1983). The POPSI manual (Open University Internal Report). Milton Keynes, UK: The Open University.Google Scholar
  9. Kimball, R. B. (1973). Self optimizing computer-assisted tutoring theory and practice (Technical report No. 206). Palo Alto, CA: Stanford University, Institute for Mathematical Studies in the Social Sciences.Google Scholar
  10. Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980a). Expert and novice performance in solving physics problems. Science, 208, 1335–1342.CrossRefGoogle Scholar
  11. Larkin, J. H., McDermott, J., Simon, D. P., & Simon, H. A. (1980b). Models of competence. Cognitive Science, 4, 317–345.CrossRefGoogle Scholar
  12. Larkin, K. H. (1978). An analysis of adult procedure synthesis in fraction problems (ICAI report No. 14). Cambridge, MA: Bolt, Beranek and Newman.Google Scholar
  13. Luger, G. (1980). Human problem solving and the Mecho trace. Proceedings of the Artificial Intelligence Society of Britain. Amsterdam.Google Scholar
  14. Marion, J. B. (1978). Physics and the physical universe. Orlando, FL: Academic Press.Google Scholar
  15. McDermott, J., & Larkin, J. H. (1978). Representing textbook physics problems. Proceedings of the 2nd national conference of the Canadian Society for Computational Studies of Intelligence (pp. 156–164). Toronto: University of Toronto Press.Google Scholar
  16. McDermott, L. C. (1984, July). Research on conceptual understanding in mechanics. Physics Today, 24–32.Google Scholar
  17. Miller, G. A. (1956). The magical number seven plus or minus two: Some limits on our capacity for processing information. Psychological Review, 63, 81–97.CrossRefGoogle Scholar
  18. O’Shea, T., & Sleeman, D. H. (1973). A design for an adaptive self improving teaching system. In J. Rose (Ed.), Advances in cybernetics. New York: Gordon & Breach.Google Scholar
  19. Priest, T. (1981). A design for an intelligent mechanics tutor. In Proceedings of CAL 81. Leeds, England:Google Scholar
  20. Reif, F., & Heller, J. (1981). Knowledge structure in physics. Internal Report, Sesame project. Berkeley: University of California.Google Scholar
  21. Scanlon, E., Hawkridge, C., Evertsz, R., & O’Shea, T. (1984). Novice physics problem solving behavior. In T. O’Shea (Ed.), Advances in artificial intelligence (pp. 245–256). North Holland: Elsevier.Google Scholar
  22. Scanlon, E., Hawkridge, C., & O’Shea, T. (1983). Modeling physics problem solving (Open University C.A.L. technical report No. 36). Milton Keynes, UK: The Open University.Google Scholar
  23. Scanlon, E., & O’Shea, T. (1982). How novices solve physics problems. In Proceedings of the fourth cognitive science conference (pp. 131–134). Ann Arbor, MI.Google Scholar
  24. Scanlon, E., & O’Shea, T. (1985). Eight production rule models for a graph interpretation and equation manipulation problem. (Open University C.A.L. working paper No. 54). Milton Keynes, UK: The Open University.Google Scholar
  25. Simon, D. P., & Simon, H. A. (1978). Individual differences in solving physics problems. In R. S. Siegler (Ed.), Children’s thinking: What develops? Hillsdale, NJ: Lawrence Erlbaum Assoc.Google Scholar
  26. Sleeman, D.H. (1982). Malrules in children’s algebra. In Proceedings of Artificial Intelligence Society of Britain. Orsay, Paris: Proc. Society for the Study of Artificial Intelligence and the Simulation of Behavior.Google Scholar
  27. Sleeman, D. H., & Brown, J. S. (Eds.) (1982). Intelligent tutoring systems. Orlando, FL: Academic Press.Google Scholar
  28. Trowbridge (1979). An investigation of student understanding of the concept of velocity in one dimension. American Journal of Physics, 48, 1000.Google Scholar
  29. VanLehn, K. (1983). Felicity conditions for human skill acquisition; Validating an AI based theory (Xerox PARC technical report, CIS-21). Palo Alto, CA: Xerox.Google Scholar
  30. Young, R. M., & O’Shea, T. (1981). Errors in children’s subtraction. Cognitive Science, 5, 153–177.CrossRefGoogle Scholar

Additional References

  1. de Kleer, J., & Brown, J. S., (1985). A qualitative physics based on confluences. In D. G. Bobrow (Ed.), Qualitative reasoning about physical systems (pp. 7–85). Cambridge, MA: MIT Press.Google Scholar
  2. di Sessa, A. A. (1982). Unlearning Aristotelian physics: A study of knowledge-based learning. Cognitive Science, 6, 37–75.MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag New York Inc. 1988

Authors and Affiliations

  • Eileen Scanlon
  • Tim O’Shea

There are no affiliations available

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