Abstract
In this paper, we make a first effort to define requirements for knowledge representation (KR) in an ITS. The requirements concern all stages of an ITS’s life cycle (construction, operation and maintenance), all types of users (experts, engineers, learners) and all its modules (domain knowledge, user model, pedagogical model). We also briefly present and compare various KR formalisms used (or that could be used) in ITSs as far as the specified KR requirements are concerned. It appears that various hybrid approaches to knowledge representation can satisfy the requirements in a greater degree than that of single representations. Another finding is that there is not a hybrid formalism that can satisfy the requirements of all of the modules of an ITS, but each one individually. So, a multi-paradigm representation environment could provide a solution to requirements satisfaction.
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References
Gallant, S.I.: Neural Network Learning and Expert Systems. MIT Press, Cambridge (1993)
Golding, A.R., Rosenbloom, P.S.: Improving accuracy by combining rule-based and casebased reasoning. Artificial Intelligence 87, 215–254 (1996)
Guin-Duclosson, N., Jean-Danbias, S., Norgy, S.: The AMBRE ILE: How to Use Case- Based Reasoning to Teach Methods. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 782–791. Springer, Heidelberg (2002)
Hatzilygeroudis, I., Prentzas, J.: Neurules: Improving the Performance of Symbolic Rules. International Journal on Artificial Intelligence Tools 9, 113–130 (2000)
Hatzilygeroudis, I., Prentzas, J.: Using a Hybrid Rule-Based Approach in Developing an Intelligent Tutoring System with Knowledge Acquisition and Update Capabilities. Journal of Expert Systems with Applications 26, 477–492 (2004)
Magoulas, G.D., Papanikolaou, K.A., Grigoriadou, M.: Neuro-fuzzy Synergism for Planning the Content in a Web-based Course. Informatica 25, 39–48 (2001)
Nkambou, R.: Managing Inference Process in Student Modeling for Intelligent Tutoring Systems. In: Proceedings of the Eleventh IEEE International Conference on Tools with Artificial Intelligence, IEEE Computer Society Press, Los Alamitos (1999)
Prentzas, J., Hatzilygeroudis, I., Garofalakis, J.: A Web-Based Intelligent Tutoring System Using Hybrid Rules as its Representational Basis. In: Cerri, S.A., Gouardéres, G., Paraguaçu, F. (eds.) ITS 2002. LNCS, vol. 2363, pp. 119–128. Springer, Heidelberg (2002)
Vanlehn, K., Zhendong, N.: Bayesian student modeling, user interfaces and feedback: a sensitivity analysis. International Journal of AI in Education 12, 155–184 (2001)
Vassileva, J.: Dynamic Course Generation on the WWW. British Journal of Educational Technologies 29, 5–14 (1998)
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Hatzilygeroudis, I., Prentzas, J. (2004). Knowledge Representation Requirements for Intelligent Tutoring Systems. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2004. Lecture Notes in Computer Science, vol 3220. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-30139-4_9
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DOI: https://doi.org/10.1007/978-3-540-30139-4_9
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22948-3
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