Abstract
Changes in the user’s knowledge represent an important factor to be considered, particularly in the dialogue between a tutoring system and a student. In previous work we have proposed a representation formalism for describing the status and the evolution over time of a temporal student model. The specific goal of this paper is to show what algorithms can be used to manage such a temporal student model. The use of temporal constraints allows a system to cope with uncertainty and incompleteness in the information available about the student’s knowledge through the description of temporal information on different levels of precision. Furthermore, nonmonotonic inferences are exploited in order to extend the temporal information available about the student’s knowledge. Finally, by introducing suitable temporal constraints into the student model, we handle in a uniform and elegant way the problem of the existence of possible contradictions in the student’s knowledge.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Dean, T., and McDermott, D. (1987). Temporal data base management. Artificial Intelligence 1–55.
Dechter, R., Meiri, I., and Pearl, J. (1991). Temporal constraint networks. Artificial Intelligence 49:61–95.
Errico, B. (1996). Student modeling in the situation calculus. In Brna, P., Paiva, A., and Self, J., eds., Proceedings of the European Conference on Artificial Intelligence in Education.
de Kleer, J. (1986). An assumption-based TMS. Reprinted in Ginsberg, M. L., ed., Readings in Nonmonotonic Reasoning. Morgan Kaufmann, 1987. 280–297.
de Kleer, J., and Williams, B. (1987). Diagnosing multiple faults. Artificial Intelligence 32:97–130.
Gärdenfors, P. (1992). Belief revision. In Handbook of Logic in AI and Logic Programming.
Giangrandi, P., and Tasso, C. (1995). Truth maintenance techniques for modeling student’s behaviour. Journal of AI and Education 6(2/3): 153–202.
Giangrandi, P., and Tasso, C. (1996). Modeling the temporal evolution of student’s knowledge. In Brna, P., Paiva, A., and Self, J., eds., Proceedings of the European Conference on Artificial Intelligence in Education. 184–190.
Huang, X. (1994). Modeling a student’s inconsistent beliefs and attention. In Greer J. E., and McCalla, G. I., eds. (1994). Student Modeling: The Key to Individualized Knowledge-Based Instruction. Springer. 267–279.
Huang, X., McCalla, G. I., Greer J. E., and Neufeld, E. (1991). Revising deductive knowledge and stereotypical knowledge in a student model. User Modeling and User-Adapted Interaction 1:87–115.
Kono Y., Ikeda, M. and Mizoguchi, R. (1994). THEMIS: A nonmonotonic inductive student modeling system. Journal of AI and Education 5:371–413.
McCarthy, J., and Hayes P. J. (1969). Some philosophical problems from the standpoint of artificial intelligence. Reprinted in Ginsberg, M. L., ed., Readings in Nonmonotonic Reasoning. Morgan Kaufmann, 1987. 26–45.
Paiva, A., and Self, J. (1994). A learner model reason maintenance system. In Proceedings of ECAl ’94.
Paiva, A., Self, J., and Hartley, R. (1994). On the dynamics of learner models. In Proceedings of ECAI ’94, 163–167.
Reiter, R. (1980). A logic for default reasoning. Ginsberg, M. L., ed., Readings in Nonmonotonic Reasoning. Morgan Kaufmann, 1987. 68–93.
Reiter, R., (1987). A theory of diagnosis from first principles. Artificial Intelligence 32:57–95.
Van Beek, P., (1990). Reasoning about qualitative temporal information. In Proceedings of AAAI-90, 728–734.
Vilain, M., Kautz, H., van Beek, P. (1990). Constraint propagation algorithms for temporal reasoning: A revised report. Reprinted in Weld, D., and de Kleer J., eds., Readings in Qualitative Reasoning about Physical Systems. Morgan Kaufmann.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 1997 Springer-Verlag Wien
About this paper
Cite this paper
Giangrandi, P., Tasso, C. (1997). Managing Temporal Knowledge in Student Modeling. In: Jameson, A., Paris, C., Tasso, C. (eds) User Modeling. International Centre for Mechanical Sciences, vol 383. Springer, Vienna. https://doi.org/10.1007/978-3-7091-2670-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-7091-2670-7_41
Publisher Name: Springer, Vienna
Print ISBN: 978-3-211-82906-6
Online ISBN: 978-3-7091-2670-7
eBook Packages: Springer Book Archive