Abstract
Probability-based inference in complex networks of interdependent variables is an active topic in statistical research, spurred by such diverse applications as forecasting, pedigree analysis, troubleshooting, and medical diagnosis. This paper concerns the role of Bayesian inference networks for updating student models in intelligent tutoring systems (ITSs). Basic concepts of the approach are briefly reviewed, but the emphasis is on the considerations that arise when one attempts to operationalize the abstract framework of probability-based reasoning in a practical ITS context. The discussion revolves around HYDRIVE, an ITS for learning to troubleshoot an aircraft hydraulics system. HYDRIVE supports generalized claims about aspects of student proficiency through probabilitybased combination of rule-based evaluations of specific actions. The paper highlights the interplay among inferential issues, the psychology of learning in the domain, and the instructional approach upon which the ITS is based.
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Andreassen, S., Woldbye, M., Falck, B., and Andersen, S.K. (1987). MUNIN: A causal probabilistic network for interpretation of electromyographic findings.Proceedings of the 10th International Joint Conference on Artificial Intelligence (pp. 366–372). San Mateo, CA: Morgan Kaufmann.
Appelt, D.E., and Pollack, M. E. (1992). Weighted abduction for plan ascription.User Modeling and User-Adapted Interaction,2, 1–25.
Bauer, M. (in press). A Dempster-Shafer approach to modeling agent preferences for plan recognition.User Modeling and User-Adapted Interaction, in this special issue.
Brown, J. S., Burton, R. R. and Bell, A. G. (1974). SOPHIE: A sophisticated instructional environment for teaching electronic troubleshooting.BBN REPORT 2790. Cambridge, MA: Bolt Beranek and Newman, Inc.
Cheeseman, P. (1986). Probabilistic versus fuzzy reasoning. In L.N. Kanal and J.F. Lemmer (Eds.),Uncertainty in artificial intelligence (pp. 85–102). Amsterdam: North-Holland.
Corbett, A.T., and Anderson, J.R. (1995). Model tracing: Modeling the acquisition of procedural knowledge.User Modeling and User-Adapted Interaction,4, 253–278.
de Finetti, B. (1974).Theory of probability (Volume 1). London: Wiley.
de Rosis, F., Pizzutilo, Russo, A., Berry, D.C., and Molina, F.J.N. (1992). Modeling the user knowledge by belief networks.User Modeling and User-Adapted Interaction,2, 367–388.
Desmarais, M.C., Giruox, L., and Larochelle, S. (1993). An advice-giving interface based on plan-recognition and user-knowledge assessment.International Journal of Man-Machine Studies,39, 901–924.
Gitomer, D.H., Cohen, W., Freire, L., Kaplan, R., Steinberg, L., and Trenholm, H. (1992).The software generalizability of HYDRIVE (Armstrong Laboratories Progress Report). Princeton, NJ: Educational Testing Service.
Gitomer, D.H., Steinberg, L.S., and Mislevy, R.J. (1995). Diagnostic assessment of trouble-shooting skill in an intelligent tutoring system. In P. Nichols, S. Chipman, and R. Brennan (Eds.),Cognitively diagnostic assessment (pp. 73–101). Hillsdale, NJ: Erlbaum.
Good, I.J. (1950).Probability and the weighting of evidence. London: Griffin; New York: Hafner.
Good, I.J. (1971). The probabilistic explication of information, evidence, surprise, causality, explanation, and utility. In V.P. Godambe and D.A. Sprott (Eds.),Foundations of statistical inference (pp. 108–141). Toronto: Holt, Rinehart, and Winston.
Jameson, A. (1992). Generalizing the double-stereotype approach: A psychological perspective.Proceedings of the Third International Workshop on User Modeling (69-83), Dagstuhl, Germany, August 1992.
Kieras, D.E. (1988). What mental model should be taught: Choosing instructional content for complex engineered systems. In M.J. Psotka, L.D. Massey, and S.A. Mutter (Eds.),Intelligent tutoring systems: Lessons learned (pp. 85–111). Hillsdale, NJ: Lawrence Erlbaum.
Kimball, R. (1982). A self-improving tutor for symbolic integration. In D. Sleeman and J.S. Brown (Eds.),Intelligent tutoring systems (pp. 283–307). London: Academic Press.
Lauritzen, S.L., and Spiegelhalter, D.J. (1988). Local computations with probabilities on graphical structures and their application to expert systems (with discussion).Journal of the Royal Statistical Society, Series B,50, 157–224.
Lesgold, A. M., Eggan, G., Katz, S., and Rao, G. (1992). Possibilities for assessment using computerbased apprenticeship environments. In J. W. Regian and V.J. Shute (Eds.),Cognitive approaches to automated instruction (pp. 49–80). Hillsdale, NJ: Lawrence Erlbaum.
Martin, J.D., and VanLehn, K. (1995). A Bayesian approach to cognitive assessment. In P. Nichols, S. Chipman, and R. Brennan (Eds.),Cognitively diagnostic assessment (pp. 141–165). Hillsdale, NJ: Erlbaum.
Means, B. and Gott, S.P. (1988). Cognitive task analysis as a basis for tutor development: Articulating abstract knowledge representations. In M.J. Psotka, L.D. Massey, and S.A. Mutter (Eds.),Intelligent tutoring systems: Lessons learned (pp. 35–58). Hillsdale, NJ: Erlbaum.
Mislevy, R.J. (1994a). Evidence and inference in educational assessment.Psychometrika,59, 439–483.
Mislevy, R.J. (1994b). Virtual representation of IID observations in Bayesian belief networks.ETS Research Memorandum 94-13-ONR. Princeton, NJ: Educational Testing Service.
Mislevy, R.J. (1995). Probability-based inference in cognitive diagnosis. In P. Nichols, S. Chipman, and R. Brennan (Eds.),Cognitively diagnostic assessment (pp. 43–71). Hillsdale, NJ: Erlbaum.
Neapolitan, R.E. (1990).Probabilistic reasoning in expert systems: Theory and algorithms. New York: Wiley.
Noetic Systems, Inc. (1991). ERGO [computer program], Baltimore, MD: Author.
Pearl, J. (1988).Probabilistic reasoning in intelligent systems: Networks of plausible inference. San Mateo, CA: Kaufmann.
Savage, L.J. (1961). The foundations of statistics reconsidered. In J. Neyman (Ed.),Proceedings of the Fourth Berkeley Symposium of Mathematical Statistics and Probability, Vol. I (pp. 575–586). Berkeley: University of California Press.
Schum, D.A. (1979). A review of a case against Blaise Pascal and his heirs.Michigan Law Review,77, 446–483.
Schum, D.A. (1994).The evidential foundations of probabilistic reasoning. New York: Wiley.
Shafer, G. (1976).A mathematical theory of evidence. Princeton: Princeton University Press.
Spiegelhalter, D.J. (1989). A unified approach to imprecision and sensitivity of beliefs in expert systems. In L.N. Kanal, J. Lemmer, and T.S. Levitt (Eds.),Artificial intelligence and statistics (pp. 47–68). Amsterdam: North-Holland.
Spiegelhalter, D.J., and Cowell, R.G. (1992). Learning in probabilistic expert systems. In J.M. Bernardo, J.O. Berger, A.P. Dawid, and A.F.M. Smith (Eds.),Bayesian Statistics 4 (pp. 447–465). Oxford, U.K.: Oxford University Press.
Spiegelhalter, D.J., Dawid, A.P., Lauritzen, S.L., and Cowell, R.G. (1993). Bayesian analysis in expert systems.Statistical Science,8, 219–283.
Villano, M. (1992). Probabilistic student models: Bayesian belief networks and knowledge space theory. In C. Frasson, G. Gauthier, and G. McCalla (Eds.),Intelligent Tutoring Systems: Proceedings of the Second International Conference, ITS '92 (491–498), Montreal, June 1992.
von Winterfeldt, D., and Edwards, W. (1986).Decision analysis and behavioral research. Cambridge: Cambridge University Press.
Wenger, E. (1987).Artificial intelligence and tutoring systems: Computational and cognitive approaches to the communication of knowledge. Los Altos, CA: Morgan Kaufmann.
Zadeh, L.A. (1965). Fuzzy sets.Information and Control,8, 338–353.
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Mislevy, R.J., Gitomer, D.H. The role of probability-based inference in an intelligent tutoring system. User Model User-Adap Inter 5, 253–282 (1995). https://doi.org/10.1007/BF01126112
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DOI: https://doi.org/10.1007/BF01126112