Student and expert modelling for simulation-based training: A cost effective framework

  • Kalina Yacef
  • Leila Alem
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1086)


The complexity of student models and expert models is often an obstacle in Intelligent Tutoring Systems research, particularly in the field of simulation-based training where the learner's actions are less contained and can therefore lead to unpredictable paths. In this paper we propose a framework for student modelling and expert modelling in the context of the design of a simulation-based learning system for training of operational skills which is incremental and cost effective as it allows the evaluation of student performance as well as the evaluation of his knowledge in certain aspects of the air traffic control activity. This framework is currently used in the design of an intelligent simulation-based training system for air traffic controllers.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. L. Alem (1995), ITS design methodology based on a model of the activity, Proceedings of Computer in Education, Singapore.Google Scholar
  2. L. Alem & R. Keeling (1996), Intelligent simulation environments: an application to air traffic control, Proceedings of SimTect'96, Melbourne, Australia.Google Scholar
  3. L. Alem & M. Lee, 1994, “An intelligent tutoring system for sonar application in Australia”, Proceedings of The second World Congress on Expert Systems, Portugal.Google Scholar
  4. J.R. Anderson (1988), The Expert Module, in Foundations of Intelligent Tutoring Systems, M. Polson & J. Richardson, Lawrence Erlbaum Associate Publishers, Hilldale, New Jersey.Google Scholar
  5. A. Bisseret (1979), Utilisation de la theorie de la detection du signal pour l'etude de decisions operatives: effet de l'experience des operateurs, Rapport INRIA Rocquencourt CO 7911R60, France.Google Scholar
  6. J.S. Brown, R.R. Burton & A.G. Bell (1975), SOPHIE: a step towards a reactive learning environment. International Journal Man-Machine Studies, 1975, vol 7, pp 675–696.Google Scholar
  7. M.L Burger & J.F DeSoi (1992), The cognitive apprenticeship analogue: A strategy for using ITS technology for the delivery of instruction and as a research tool for the study of teaching and learning, International Journal of Man-machine Studies, 36, 775–795.Google Scholar
  8. J.R. Carbonell (1970), Mixed-Initiative Man-Computer Instructional Dialogues, Doctoral dissertation, Massachusetts Institutes of Technology, Cambridge, Massachusetts.Google Scholar
  9. R.W Chu, C.M, Mitchell & P.M Jones (1995), Using the Operator Function Model and OFMspert as the basis for an intelligent tutoring system: towards a tutor/aid paradigm for operators of supervisory control systems, IEEE transactions on systems, man and cybernetics, July 1995 vol 27, No 7.Google Scholar
  10. M. Cialdea (1991), Meta-reasoning and student modelling, New directions for Intelligent Tutoring systems, E. Costa eds, Berlin: Springer-Verlag, pp 183–197.Google Scholar
  11. M. Cox (1992), The cognitive aspects of the air traffic control task: a literature review, IAM report 718, RAF Institute of A viation Medicine, Farnborough, UK.Google Scholar
  12. P. Eliot & B.P. Woolf (1994), Reasoning about the user within a simulation-based real-time training system, Proceedings of User Modeling.Google Scholar
  13. T. de Jong (1991), Learning and Instruction with computer simulations, Education & Computing 6, pp 217–229.Google Scholar
  14. P. Duchastel (1991). Instructional strategies for simulation-based training, Journal of Educational Technology Systems, 19, 265–276.Google Scholar
  15. Eurocontrol Experimental Centre, B.P. 15, 91222 Bretigny sur Orge cedex, France.Google Scholar
  16. L. Gugerty & K. Hicks (1993), Non-diagnostic intelligent tutoring systems, Proceedings of the 15th interservice/industry training systems and education conference, Orlando, Florida, pp 450–459.Google Scholar
  17. J.D. Hollan, E.L. Hutchins & L. Weitzman (1984), STEAMER: an interactive inspectable simulation-based training system, The AI Magazine, Summer 1984, pp 15–27.Google Scholar
  18. V.D. Hopkin (1980), The measurement of the air traffic controller, Human factors, 22(5), 547–560.Google Scholar
  19. S. Katz, A. Lesgold, G. Eggan & M. Gordin (1992), Modelling the student in SHERLOCK II, Proceedings of AI and Education, vol 3, N. 4.Google Scholar
  20. M. Leroux (1992), How to define a philosophy of automation in ATC, April 1992, CENA document AG-SO-008-T20D R 01.0, France.Google Scholar
  21. B. Lucat (1998), A new approach to ACT simulation, ICAO Bulletin, May 1988, pp 30–32.Google Scholar
  22. D. Newman, M. Grignetti, M. Gross & L.D. Massey (1989), Intelligent conduct of fire trainer: Intelligent technology applied to simulator-based training. Machine-Mediated Learning, 3, 29–39.Google Scholar
  23. S. Ohlsson (1986), Some principles of intelligent tutoring, Instructional science 14 293–326, Amsterdam, Elsevier Science Publishers B.V.Google Scholar
  24. A. Paiva & J. Self (1995), TAGUS-A user and learner modeling workbench, User modeling and User-adapted interaction, 4:197–226, Kluwer Academic Publishers.Google Scholar
  25. M.C Polson & J. Richardson (1988), Foundations of Intelligent Tutoring systems, Hillsdale, New Jersey, Lawrence Erlbaum Associates Publishers.Google Scholar
  26. P. Reiman (1993), Supporting exploratory learning by providing remindings automatically, Proceedings of AI-ED 93 workshop: Simulations for Learning: Design, Development and Use.Google Scholar
  27. S. Sebillotte & B. Alonso (1994), Description MAD de la tache de “controle aerien” executee par deux controleurs, Document Interne INRIA Rocquencourt, Le Chesnay, France.Google Scholar
  28. J.A. Self (1989), The case of formalising student models (and intelligent tutoring generally). Proceedings of AI and Education, Amsterdam.Google Scholar
  29. J. Self (1995), The ebb and flow of student modeling, Invited talk at Computer and Education Dec. 1995, Singapore.Google Scholar
  30. E. Wenger (1987), Artificial Intelligence and Tutoring systems, Morgan Kaufmann publishers, Los Altos, CA.Google Scholar
  31. D.D. Woods (1988), Coping with complexity: the psychlogy of human behavior in complex systems, Tasks, errors and mental models, Goodstein, Andersen and Olsen eds. London: Taylor & Francis, pp 128–148.Google Scholar
  32. D. Zhang & L. Alem (1996), Case-based exercise design for ATC training, Proceedings of ITS'96 conference, Montreal, Canada.Google Scholar

Copyright information

© Springer-Verlag 1996

Authors and Affiliations

  • Kalina Yacef
    • 1
    • 2
    • 3
  • Leila Alem
    • 2
    • 3
  1. 1.Thomson Radar Australia Corp. & Lab. d'Intelligence Artificielle de Paris 5France
  2. 2.CSIRO DITNorth RydeAustralia
  3. 3.CRC-IDSCarltonAustralia

Personalised recommendations