Abstract
Model-tracing tutors have consistently been among the most effective class of intelligent learning environments. Across a number of empirical studies, these tutors have shown students can learn the tutored domain better or in a shorter amount of time than traditionally taught students (Anderson, Boyle, Corbett, & Lewis, 1990). Unfortunately, the creation of these tutors, particularly the production system component, is a time-intensive task, requiring knowledge that lies outside the tutored domain. This outside knowledge—knowledge of programming and cognitive science—prohibits domain experts from being able to construct effective, model-tracing tutors for their domain of expertise. This paper reports on a system, referred to as Demonstr8 (pronounced “demonstrate”), which attempts to reduce the outside knowledge required to construct a model-tracing tutor, within the domain of arithmetic. By utilizing programming by demonstration techniques (Cypher, 1993; Myers, McDaniel, & Kosbie, 1993) coupled with a mechanism for abstracting the underlying productions (the procedures to be used by the tutor and learned by the student), the author can interact with the interface the student will use, and the productions will be inferred by the system. In such a way, a domain expert can create in a short time a model-tracing tutor with the full capabilities implied by such a tutor—a production system that monitors the student’s progress at each step in solving the problem and gives feedback when requested or necessary, in either an immediate or delayed manner.
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References
Anderson, J. R. (1992). Intelligent tutoring and high school mathematics. In C. Fasson, G. Gauthier, & G. I. McCalla (Eds.). Proceedings of the Second International Conference on Intelligent Tutoring Systems. Spring–Verlag: Berlin, Germany.
Anderson, J. R. (1993). Rules of the Mind. Hillsdale, NJ: Lawrence Erlbaum Associates.
Anderson, J. R., Boyle, C. F., Corbett, A. T., & Lewis, M. W. (1990). Cognitive modelling and intelligent tutoring. Artificial Intelligence, 42, 7–49.
Anderson, J. R., Boyle, C. F., & Yost, G. (1985). The geometry tutor. In Proceedings of the International Joint Conference on Artificial Intelligence—85. Los Angeles: IJCAI.
Anderson, J. R., Conrad, F. G., & Corbett, A. T. (1989). Skill acquisition and the LISP Tutor. Cognitive Science, 13, 467–506.
Anderson, J. R., Corbett, A. T., Koedinger, K. R., & Pelletier, R. (1995). The cognitive tutors: lessons learned. Journal of the Learning Sciences, Vol. 4 (2), 167–207.
Anderson, J. R., & Pelletier, R. (1991). A development system for model–tracing tutors. In Proceedings of the International Conference of the Learning Sciences (pp. 1–8 ). Evanston, IL.
Blessing, S. B. (1995). ITS authoring tools: The next generation. In J. Greer (Ed.), Proceedings of AI–ED 95–7th World Conference on Artificial Intelligence and Education (p. 567 ). Charlottesville, VA: Association for the Advancement of Computing in Education.
Blessing, S. B., & Gregoire, M. (1997). A pen–based intelligent tutor for subtraction. Unpublished manuscript.
Brown, J. S., & Burton, R. (1978). Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science, 2, 155–192.
Corbett, A. T., & Anderson, J. R. (1990). The effect of feedback control on learning to program with the LISP tutor. In Proceedings of the Twelfth Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.
Cypher, A. (1993). Watch What I Do: Programming by Demonstration. MIT Press, Cambridge, MA.
Koedinger, K. R. & Anderson, J. R. (1989). Perceptual chunks in gemoetry problem solving: A challenge to theories of skill acquisition. In Proceedings of the Eleventh Annual Conference of the Cognitive Science Society. Hillsdale, NJ: Lawrence Erlbaum Associates.
Koedinger, K. R., & Anderson, J. R. (1993a). Effective use of intelligent software in high school math classrooms. In Proceedings of the World Conference on Artificial Intelligence in Education, 1993. Charlottesville, VA: AACE.
Koedinger, K. R. & Anderson, J. R. (1993b). Reifying implicit planning in geometry: Guidelines for model–based intelligent tutoring system design. In S.P. Lajoie and S.J. Derry (Eds.) Computers as Cognitive Tools. Hillsdale, NJ: Lawrence Erlbaum Associates.
Koedinger, K. R., Aleven, V., & Heffernan, N. (2003). Toward a rapid development environment for Cognitive Tutors. Submitted to Artificial Intelligence in Education conference.
Koedinger, K. R., Anderson, J. R., Hadley, W. H. & Mark, M. A. (1997). Intelligent tutoring goes to school in the big city. International Journal of Artificial Intelligence in Education, 8, 30–43
Lewis, M. W., Milson, R., & Anderson, J. R. (1987). The Teacher’s Apprentice: Designing an intelligent authoring system for high school mathematics. In G.P. Kearsley (Ed.) Artificial Intelligence and Instruction: Applications and Methods (pp. 269–301 ). Addison–Wesley Publishing Company: Reading, MA.
Murray, T. (1999). Authoring Intelligent Tutoring Systems: Analysis of the state of the art. International Journal of Artificial Intelligence in Education. Vol. 10 (1), pp. 98–129.
Myers, B. A., McDaniel, R. G., & Kosbie, D. S. (1993). Marquise: Creating Complete User Interfaces by Demonstration. In Proceedings of INTERCHI 93: Human Factors in Computing Systems, April 24–29, 1993.
Nardi, B. A. (1993). A small matter of programming: Perspectives on end–user computing. Cambridge, MA: MIT Press.
Neves, D. M. (1978). A computer program that learns algebraic procedures by examining examples and by working test problems in a textbook. Proceedings of the Second National Conference of the Canadian Society for Computational Studies of Intelligence.
Newell, A., & Simon, H. A. (1972). Human problem solving. Englewood Cliffs, NJ: Prentice–Hall. Ritter, S., & Koedinger, K. R. (1995). Towards lightweight tutoring agents. In J. Greer (Ed.), Proceedings of AI–ED 95–7th World Conference on Artificial Intelligence and Education (p. 567 ). Charlottesville, VA: Association for the Advancement of Computing in Education.
Salvucci, D. D., & Anderson, J. R. (1998). Analogy. In J. R. Andersion & C. Lebiere (Eds.), The Atomic Components of Thought (pp. 343–383 ). Mahwah, NJ: Erlbaum.
Smith, D. C., Cypher, A., & Spohrer, J. (1994). KidSim: Programming agents without a programming language. Communications of the ACM, 37 (7), 55–67.
Woolf, B.P. & Cunningham, P.A. (1987) Multiple knowledge sources in intelligent teaching systems. IEEE Expert, Summer 1987.
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Blessing, S.B. (2003). A Programming by Demonstration Authoring Tool for Model-Tracing Tutors. In: Murray, T., Blessing, S.B., Ainsworth, S. (eds) Authoring Tools for Advanced Technology Learning Environments. Springer, Dordrecht. https://doi.org/10.1007/978-94-017-0819-7_4
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DOI: https://doi.org/10.1007/978-94-017-0819-7_4
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