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Intelligent guide: Combining user knowledge assessment with pedagogical guidance

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Intelligent Tutoring Systems (ITS 1996)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1086))

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Abstract

Despite their many successes, Intelligent Tutoring Systems (ITS) are not yet practical enough to be employed in the real world educational/training environments. We argue that this undesirable scenario can be changed by focusing on developing an ITS development methodology that transforms current ITS research to consider practical issues that are part of the main causes of underemployment of ITSs. Here we describe an ambitious research project to develop an ITS that has recently completed its first phase of development at the Computer Research Institute of Montreal. This project aims to address issues, such as, making ITS handle multiple domains, developing cost-effective knowledge assessment methodologies, organizing and structuring domains around curriculum views and addressing the needs of users by considering their immediate goals and educational/training settings. This paper concentrates on the outcomes of the first phase of our project that includes the architecture and functionality (specially user knowledge assessment and pedagogical guidance) of the Intelligent Guide.

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References

  1. Anderson, J. R., Boyle, C. F., Corbett, A. T. & Lewis, M. W. (1990). Cognitive modeling and intelligent tutoring. In Clancey, W. J. & Soloway, E. (Eds.). Artificial intelligence and learning environment (pp. 7–49). Cambridge, MA: The MIT Press.

    Google Scholar 

  2. Barr, A., Beard, M. & Alkinson, R. C. (1976). The computer as a tutorial laboratory: The Stanford BIP project. International Journal of Man-Machine Studies, 8, 567–596.

    Google Scholar 

  3. Desmarais, M. C., Maluf, A. & Liu, J. (to appear). User-expertise modeling with empirically derived probabilistic implication networks. User-Modeling and User Adaptive Interaction.

    Google Scholar 

  4. Desmarais, M., Giroux, L., Larochelle, S. & Leclerc, S. (1988). Assessing the structure of knowledge in a procedural domain. In Proceedings of the Tenth Annual Conference of the Cognitive Science Society (pp. 475–481).

    Google Scholar 

  5. Falmagne, J., Doignon, J., Koppen, M., Villano, M. & Johannesen, L. (1990). Introduction to knowledge spaces: how to build, test and search them. Psychological Review, 97, 201–224.

    Google Scholar 

  6. Goldstein, I. P. (1982). The genetic graph: A representation for the evolution of procedural knowledge. In Sleeman, D. & Brown, J. S. (Eds.), Intelligent tutoring systems (pp. 51–77). London: Academic Press, Inc.

    Google Scholar 

  7. Jones M. (1992). Instructional systems need instructional theory: Comments on a truism. In Scanion, E & O'Shea, T. (Eds.), New directions in educational technology (pp. 1–13). Berlin, Germany: Springer-Verlag.

    Google Scholar 

  8. Khuwaja, R. A. (1994). A model of tutoring: Facilitating knowledge integration using multiple models of the domain. Ph.D. Thesis, Computer Science Department, Illinois Institute of Technology, Chicago, Illinois.

    Google Scholar 

  9. Lesgold, A. (1988). Towards a theory of curriculum for use in designing intelligent instructional systems. In Mandl, H. & Lesgold, A. (Eds.), Learning issues for intelligent tutoring systems (pp. 114–137). New York: Springer-Verlag.

    Google Scholar 

  10. Murray, W. R. (1988). Control for intelligent tutoring systems: A comparison of blackboard architecture and discourse management networks. Technical Report R-6267, FMC corporation.

    Google Scholar 

  11. Pearl, J. (1988). Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. San Mateo, CA: Morgan Kaufmann.

    Google Scholar 

  12. Reigeluth, C.M. (1992). New directions for educational technology. In Scanion, E & O'Shea, T. (Eds.), New directions in educational technology (pp. 51–59). Berlin, Germany: Springer-Verlag.

    Google Scholar 

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

    Google Scholar 

  14. White, B. Y. & Frederiksen, J. R. (1990). Causal model progressions as a foundation for intelligent learning environments. In Clancey, W. J. & Soloway, E. (Eds.). Artificial intelligence and learning environment (pp.7–49). Cambridge, MA: The MIT Press.

    Google Scholar 

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Claude Frasson Gilles Gauthier Alan Lesgold

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© 1996 Springer-Verlag Berlin Heidelberg

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Khuwaja, R., Desmarais, M., Cheng, R. (1996). Intelligent guide: Combining user knowledge assessment with pedagogical guidance. In: Frasson, C., Gauthier, G., Lesgold, A. (eds) Intelligent Tutoring Systems. ITS 1996. Lecture Notes in Computer Science, vol 1086. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-61327-7_119

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  • DOI: https://doi.org/10.1007/3-540-61327-7_119

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-61327-5

  • Online ISBN: 978-3-540-68460-2

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