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Using Induction to Generate Feedback in Simulation Based Discovery Learning Environments

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1452)

Abstract

This paper describes a method for learner modelling for use within simulation-based learning environments. The goal of the learner modelling system is to provide the learner with advice on discovery learning. The system analyzes the evidence that a learner has generated for a specific hypothesis, assesses whether the learner needs support on the discovery process, and the nature of that support. The kind of advice described in this paper is general in the sense that it does not rely on specific domain knowledge, and specific in the sense that it is directly related to the learner’s interaction with the system. The learner modelling mechanism is part of the SimQuest authoring system for simulation-based discovery learning environments

Key words

Intelligent Tutoring Systems Simulation-Based Learning Scientific Discovery 

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References

  1. 1.
    Bruner, J. S. (1961). The act of discovery. Harvard Educational Review, 31, 21–32.Google Scholar
  2. 2.
    Glaser, R., Schauble, L., Raghavan, K., & Zeitz, C. (1992). Scientific reasoning across different domains. In E. de Corte, M. Linn, H. Mandl & L. Verschaffel (Eds.), Computer-based learning environments and problem solving (pp. 345–373). Berlin, Germany: Springer-Verlag.Google Scholar
  3. 3.
    Holt, P., Dubs, S., Jones, M & Greer (1994). The State of Student Modelling. In J.E. Greer, & G. I. McCalla (Eds.), Student Modelling: The key to Individualized Knowledge-Based Instruction (NATO ASI series F: Computer and Systems Series, Vol 125) (pp. 3–35). Berlin, Germany: Springer-Verlag.Google Scholar
  4. 4.
    Jonassen, D.H. (1991). Objectivism versus constructivism: Do we need a new phi-losophical paradigm? Educational technology research & development, 39, 5–14.CrossRefGoogle Scholar
  5. 5.
    Jong, T. de, Joolingen, W. R. van, Swaak, J., Veermans K., Limbach R., King S. & Gureghian D. (in press). Combining human and machine expertise for self-directed learning in simulation-based discovery environments. Journal of Computer Assisted Learning.Google Scholar
  6. 6.
    Jong, T. de, Joolingen, W. R. van, & King, S. (1997). The authoring environment Sim-Quest and the need for author support. In T. de Jong (Ed.) Supporting authors in the design of simulation based learning environments. Servive project, deliverable D 8.1. Enschede: University of Twente.Google Scholar
  7. 7.
    Jong, T. de & Joolingen, W. R. van, (in press). Discovery learning with computer simulations of conceptual domains. Review of Educational Research.Google Scholar
  8. 8.
    Jong, T. de & Njoo, M. (1992). Learning and instruction with computer simulations: learning processes involved. In E. de Corte, M. Linn, H. Mandl, & L. Verschaffel (Eds.), Computer-based learning environments and problem solving. Berlin: Springer-Verlag.Google Scholar
  9. 9.
    Joolingen, W.R. van, & Jong, T. de (1993). Exploring a domain through a computer simulation: traversing variable and relation space with the help of a hypothesis scratchpad. In D. Towne, T. de Jong & H. Spada (Eds.), Simulation-based experiential learning (pp. 191–206). Berlin, Germany: Springer-Verlag.Google Scholar
  10. 10.
    Joolingen, W.R. van, & Jong, T. de (1997). An extended dual search space model of learning with computer simulations. Instructional Science, 25, 307–346.CrossRefGoogle Scholar
  11. 11.
    Klahr, D., & Dunbar, K. (1988). Dual space search during scientific reasoning. Cognitive Science, 12, 1–48.CrossRefGoogle Scholar
  12. 12.
    Klayman, J. & Ha, Y-W. (1987). Hypothesis testing in rule discovery: strategy, structure, and content. Journal of Experimental Psychology: Learning, Memory, and Cognition, 15(4), 596–604.CrossRefGoogle Scholar
  13. 13.
    Marchionni, G. (1988) Hypermedia and learning: Freedom and Chaos. Educational Technology, 28(11), 8–12.Google Scholar
  14. 14.
    Paolucci, M., Suthers, D. & Weiner, A. (1996) Automated advice giving strategies for Scientific inquiry In C. Frasson & G. Gauthier & A. Lesgold (Eds.). Proceedings of the 3 nd In-ternational Conference on Intelligent Tutoring Systems, (pp. 372–381). Lecture Notes in Computer Science, Vol. 1086, Berlin: Springer Verlag.Google Scholar
  15. 15.
    Reimann, P. (1991). Detecting functional relations in a computerized discovery environment. Learning and instruction, 1, 45–65.CrossRefGoogle Scholar
  16. 16.
    Self J. A. (1990) Bypassing the intractable problem of student modeling. In C. Frasson & G. Gauthier (Eds.). Intelligent Tutoring Systems: At the Crossroadsof Articicial Intelligence and Education (pp. 107–123). Norwood, NJ: Ablex.Google Scholar
  17. 17.
    Shute, V.J., & Glaser, R. (1990). A large-scale evaluation of an intelligent discovery world: Smithtown. Interactive Learning Environments, 1, 51–77.CrossRefGoogle Scholar
  18. 18.
    Tsirgi, J. E. (1980) Sensible reasoning: A hypothesis about hypotheses. Child development, 51: 1–10.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1998

Authors and Affiliations

  1. 1.Faculty of Educational Science and Technology University of TwenteEnschedeThe Netherlands

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