Abstract
The use of pedagogical methods with the technologies of the information and communications produces a new quality that favours the task of generating, transmitting and sharing knowledge. Such is the case of the pedagogical effect that produces the use of the Concept Maps, which constitute a tool for the management of knowledge, an aid to personalize the learning process, to exchange knowledge, and to learn how to learn. In this paper the authors present a new approach to elaborate Intelligent Tutoring Systems, where the techniques of Concept Maps and Artificial Intelligence are combined, using Petri Nets as theoretical frame, for the student model. The pedagogical model that controls the interaction between the apprentice and the generated Intelligent Tutoring Systems is implemented by Petri Nets. The Petri Nets transitions are controlled by conditions that refer to the apprentice model. The firing of these transitions produces actions that update this apprentice model. These conditions are automatically included into the pedagogical model and the teacher has only to specify the contents of the domain model.
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
Cañas Alberto, J., Carvalho, M.: Concept Maps and AI: an Unlikely Marriage? In: SBIE 2004: Simpósio Brasileiro de Informática na Educação Manaus, Brasil (2004)
Martínez, N.: Mapas Conceptuales y Redes Bayesianas en los Sistemas de En-señanza Inteligentes. Congreso Internacional de Informática Educativa, Costa Rica (2006)
Liu, X.-Q., Wu, M., Chen, J.-X.: Knowledge aggregation and naviga-tion highlevel petri nets-based in e-learning. In: Proceedings International Conferenceon Machine Learning and Cybernetics, vol. 1, pp. 420–425 (2002)
Li, X., Yu, W.: Dynamic Knowledge Inference and Learning under Adaptive Fuzzy Petri Net Framework. IEEE Transactions on Systems, Man, and Cybernetics – Part C: Applications and reviews 30(4) (2000)
Esposito, F., Ferilli, S., Basile, A., Di Mauro, N.: Inference of abduction theories for handling incompleteness in first-order learning. Knowledge and Information Systems (KAIS) Journal, 1–17 (2006), ISSN: 0219-1377
Brusilovsky, P.: From intelligent tutoring systems to web-based education. In: 5th International Conference on ITS, Canada. LNCS. Springer, Heidelberg (2000)
Murray, T.: Authoring intelligent tutoring systems: An analysis of the state of the art. Journal of Artificial Intelligence and Education 10 (1999)
Hilera, J.: Redes neuronales artificiales: Fundamentos, modelos y aplicaciones, Ed. Addison-Wesley Iberoamericana (1995)
Yu, X., Chen, G.: Dynamic Learning Rate Optimization of the Backpropagation Algorithm. IEEE Transaction on Neural Networks 6(3), 669–677 (1995)
Bello, R.: Aplicaciones de la Inteligencia Artificial, Ediciones de la Noche, Guadalajara, Jalisco, México (2002), ISBN: 970-27-0177-5
Liu, W., Hong, J.: Re-investigating Dempster’s Idea on Evidence Combination. Knowledge and Information Systems: An International Journal 2(2), 223–241 (2000)
Cooley, J.: Data Preparation for mining World Wide Web browsing patterns. Knowledge and Information Systems (KAIS) Journal, 1–27 (1999), ISSN: 0219-1377
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
León, M., Bonet, I., García, M.M., García, Z. (2008). Combining Concept Maps and Petri Nets to Generate Intelligent Tutoring Systems: A Possible Approach. In: Gelbukh, A., Morales, E.F. (eds) MICAI 2008: Advances in Artificial Intelligence. MICAI 2008. Lecture Notes in Computer Science(), vol 5317. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-88636-5_75
Download citation
DOI: https://doi.org/10.1007/978-3-540-88636-5_75
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-88635-8
Online ISBN: 978-3-540-88636-5
eBook Packages: Computer ScienceComputer Science (R0)