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Measuring learning strategies and understanding: A research framework

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Intelligent Tutoring Systems (ITS 1992)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 608))

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Abstract

We present a framework for measurement and diagnosis using knowledge-based models. Based on formulations of knowledge-level analysis, symbol-level analysis, constructivist learning, and situated cognition, we describe the possible frames of reference and fundamental measurement approaches that may be adopted in the analysis of cognition and learning. In general, these frames of reference define how individuals, contexts, knowledge, and activity are constitutively defined. We then present several measurement models extending the objective measurement approach of Rasch models to the analysis of learning strategies and knowledge development. The structure and parameter estimates of such models can then be used in the specification of probabilistic belief networks that can perform on-line student modelling.

This research is supported by Office of Naval Research, Cognitive Science Program, grant no. N00014-91J-1523. We thank Michael Ranney and Margaret Recker for their comments.

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References

  1. R. J. Adams, M. Wilson: A random coefficients multinomial logit: A generalized approach to fitting Rasch models (under review), 1991a

    Google Scholar 

  2. R. J. Adams, M. Wilson: Hierarchical multinomial logistic regression. (in preparation), 1991b

    Google Scholar 

  3. E. B. Andersen: A general latent structure model for contingency table data. H. Wainer, S. Messick (Eds.), Principles of modern psychological measurement, Hillsdale, NJ: Lawrence Erlbaum, 1983

    Google Scholar 

  4. S. K. Andersen, K. G. Olesen, F. U. Jensen, F. Jensen: Hugin-A shell for building bayesian belief universes for expert systems. Proceedings of the Eleventh International Conference on Artificial Intelligence (pp. 1080–1085), Los Altos, CA: Morgan Kaufman, 1989

    Google Scholar 

  5. J. R. Anderson, C. F. Boyle, A. Corbett, M. W. Lewis: Cognitive modelling and intelligent tutoring. Artificial Intelligence 42 (p. 7–49), 1990

    Google Scholar 

  6. J. R. Anderson, F. G. Conrad, A. T. Corbett:. Skill acquisition and the LISP tutor. Cognitive Science 13 (pp. 467–505), 1989

    Google Scholar 

  7. D. Andrich: Rasch models for measurement. Beverly Hills, CA: SAGE Publications, 1988

    Google Scholar 

  8. R. D. Bock: Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika 46 (p. 431–444), 1981

    Google Scholar 

  9. M. T. H. Chi, M. Bassok, M. W. Lewis, P. Reiman, R. Glaser: Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science 13 (p. 145–182), 1989

    Google Scholar 

  10. K. A. Ericsson, H. A. Simon: Protocol Analysis: Verbal reports as data. Cambridge, MA: MIT Press, 1984

    Google Scholar 

  11. J. J. Gibson: The ecological approach to visual perception. Boston: Houghton Mifflin, 1979

    Google Scholar 

  12. J. G. Greeno: Number sense as situated knowing in a conceptual domain. Journal for Research in Mathemetics Education 22 (pp. 170–218), 1991

    Google Scholar 

  13. H. Kelderman: Loglinear multidimensional IRT models for polytomously scored items. Paper presented at the Fifth International Objective Measurement Workshop, University of California, Berkeley, 1989

    Google Scholar 

  14. D. H. Krantz: Conjoint measurement: The Luce-Tukey axiomatization and some extensions. Journal of Mathematical Psychology 1 (pp. 248–277), 1964

    Google Scholar 

  15. R. D. Luce, J. W. Tukey:. Simultaneous conjoint measurement: A new type of fundamental measurement. Journal of Mathematical Psychology 1 (pp. 1–27), 1964

    Google Scholar 

  16. R. J. Mislevy, K. Yamamoto, S. Anacker: Toward a test theory for assessing student understanding. Princeton, NJ: Educational Testing Service, 1990

    Google Scholar 

  17. J. Moore, A. Newell: How can MERLIN understand? In L. Gregg (Ed.), Knowledge and cognition (pp. 201–252). Hillsdale, NJ: Lawrence Erlbaum, 1973

    Google Scholar 

  18. A. Newell: Unified theories of cognition. Cambridge, MA: Harvard University Press, 1990

    Google Scholar 

  19. A. Newell, H. A. Simon: Human problem solving. Englewood Cliffs, NJ: Prentice Hall, 1972

    Google Scholar 

  20. J. Pearl: Probabilistic reasoning in intelligent systems: Networks of plausible inference. Los Altos, CA: Morgan Kaufman, 1988

    Google Scholar 

  21. R. Perline, B. D. Wright, H. Wainer: The Rasch model as additive conjoint measurement. Applied Psychological Measurement 3 (pp. 237–255), 1979

    Google Scholar 

  22. P. Pirolli: Effects of examples and their explanations in a lesson on recursion: A production system analysis. Cognition and Instruction 8 (207–259), 1991

    Google Scholar 

  23. P. Pirolli, K. Bielaczyc: Empirical analyses of self-explanation and transfer in learning to program. Proceedings of the Annual Conference of the Cognitive Science Society (pp. 450–457). Hillsdale, NJ: Lawrence Erlbaum, 1989

    Google Scholar 

  24. P. Pirolli, M. Recken Knowledge construction and transfer using an intelligent tutoring system: The role of examples, self-explanation, practice, and reflection (Tech, Rep. No. CSM-1). University of California, 1991

    Google Scholar 

  25. G. Rasch: Probabilistic models for some intelligence and attainment tests. Copenhagen: Danish Institute for Educational Research, 1960

    Google Scholar 

  26. V. J. Shute, R. Glaser: Large-scale evaluation of an intelligent tutoring system: Smithtown. Interactive Learning Environments 1 (pp. 51–76), 1990

    Google Scholar 

  27. K. VanLehn: Student modelling. In M. C. Polson, J. J. Richardson (Ed.), Foundations of intelligent tutoring systems (pp. 479–530). Hillsdale, NJ: Lawrence Erlbaum, 1988

    Google Scholar 

  28. E. Wenger: Artificial intelligence and tutoring systems. Los Altos, CA: Morgan Kaufman, 1987

    Google Scholar 

  29. M. Wilson: An extension of the partial credit model to incorporate diagnostic information. Paper presented at the Annual Meeting of the National Council for Measurement in Education, Boston, MA, 1990

    Google Scholar 

  30. M. Wilson: To describe their performances well: Contributions to a new psychometrics. Paper presented at the Natinal Academy of Education Spencer Fellows Forum, Boston, MA, 1991

    Google Scholar 

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Authors and Affiliations

  1. School of Education, University of California, Berkeley, 94720, Berkeley, CA

    Peter Pirolli & Mark Wilson

Authors
  1. Peter Pirolli
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  2. Mark Wilson
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Editor information

Claude Frasson Gilles Gauthier Gordon I. McCalla

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© 1992 Springer-Verlag Berlin Heidelberg

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Pirolli, P., Wilson, M. (1992). Measuring learning strategies and understanding: A research framework. In: Frasson, C., Gauthier, G., McCalla, G.I. (eds) Intelligent Tutoring Systems. ITS 1992. Lecture Notes in Computer Science, vol 608. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-55606-0_64

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  • DOI: https://doi.org/10.1007/3-540-55606-0_64

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-55606-0

  • Online ISBN: 978-3-540-47254-4

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