AIMSA 2000: Artificial Intelligence: Methodology, Systems, and Applications pp 221-231 | Cite as
Maintaining a Jointly Constructed Student Model
Conference paper
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Abstract
Allowing the student to have some control over the diagnosis inspecting and changing the model the system has made of him is a feasible approach in student modelling which tracks the dynamics of student behaviour and provides for reflective learning. We present an approach for maintaining the student model in interactive diagnosis where a computer and a student discuss about the student’s knowledge. A belief modal operator is adapted to model the knowledge of the learner and to help in maintaining the interaction between the computer system and the learner. A mechanism for finding agreements and conflicts between system and learner’s views is described.
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References
- 1.J.A. Self, The defining characteristics of intelligent tutoring systems research: ITSs care, precisely, International Journal of Artificial Intelligence in Education, 10, 350–364, (1999).Google Scholar
- 2.J.A. Self, Bypassing the intractable problem of student modelling, in C. Frasson & G. Gauthier eds., Intelligent Tutoring Systems: At the Crossroad of Artificial Intelligence and Education, Ablex, NJ, 1990.Google Scholar
- 3.S. Bull, Collaborative Student Modelling in Foreign Language Learning, PhD Thesis, University of Edinburgh, 1997.Google Scholar
- 4.R. Morales, H. Pain, S. Bull & J. Kay, Proceedings of the Workshop on Open, Interactive, and Other Overt Approaches to Learner Modelling, Le Mans, France, July 1999.Google Scholar
- 5.A. Kobsa, User modelling in dialogue systems: potentials and hazards, AI & Society, 1, 214–240, (1990).Google Scholar
- 6.P. Brusilovsky, Methods and Techniques of Adaptive Hypermedia. User Modeling and User-Adapted Interaction, 6(2/3), 87–129, (1996).CrossRefGoogle Scholar
- 7.V. Dimitrova, J.A. Self & P. Brna, The interactive maintenance of open learner models, in Lajoie S.P. and Vivet M., eds., Proceedings of the 9th conference of AI in Education, Frontiers in AI and Applications, vol. 50, IOS Press, pp. 405–412, 1999.Google Scholar
- 8.A. Paiva and J.A. Self, TAGUS-a user and learner modelling workbench, User Modeling and User-Adapted Interaction, 4, 197–226, (1995).CrossRefGoogle Scholar
- 9.J. Kay, The UM toolkit for cooperative user modelling, User Modeling and User-Adapted Interaction, 4, 149–196, (1995).CrossRefGoogle Scholar
- 10.J.A. Self, Computational Mathetics, http://www.cbl.leeds.ac.uk/~jas/cm.html.
- 11.R. Fagin, J.Y. Halpern, Y. Moses & M.Y. Vardi, Reasoning about Knowledge, The MIT Press, Cambridge, 1995.MATHGoogle Scholar
- 12.J. Taylor, J. Carletta, C. Mellish, Requirements for belief models in cooperative dialogue, User Modelling and User-Adapted Interaction, 6, 23–68, (1996).CrossRefGoogle Scholar
- 13.A. Ballim and Y. Wilks, Artificial Believers: The Ascription of Belief, Erlbaum, Hillsdale, NJ, 1991.Google Scholar
- 14.A. Kobsa, and W. Pohl,, The user modeling shell system BGP-MS. User Modeling and User-Adapted Interaction 4(2), 59–106, (1995).CrossRefGoogle Scholar
- 15.S. Bull and P. Brna, Enhancing peer interaction in the Solar system, in Brna P., Baker M. and Stenning K. eds., Roles of communicative interaction in learning to model in Mathematics and Science, Proceedings of the C-LEMMAS conference, April 1999.Google Scholar
- 16.V. Dimitrova, J.A. Self & P. Brna, STyLE-OLM-an interactive diagnosis tool in a terminology learning environment, in the Proceedings of the Workshop on Open, Interactive, and Other Overt Approaches to Learner Modelling, Le Mans, France, July 1999.Google Scholar
- 17.J. Sowa, Conceptual graphs: draft proposed American national standard, in Proceedings of ICCS-99-Conceptual Structures: Standards and Practices, Lecture Notes of Artificial Intelligence 1640, Springer Verlag, Berlin, pp. 1–65, 1999.CrossRefGoogle Scholar
- 18.G. Angelova, A. Nenkova, Sv. Boycheva, and T. Nikolov, CGs as a Knowledge Representation Core in a Complex Language Learning Environment, in the Proceedings of ICCS-2000, Darmstadt, Germany, August 2000, to appear.Google Scholar
- 19.J. Levin & J. Moore, Dialogue games: meta-communication structures for natural language interaction, Cognitive Science, 1, 395–420, (1977).CrossRefGoogle Scholar
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