Knowledge representation requirements for intelligent tutoring systems

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 3220, Issue , 2004, Pages 87-97

  Hatzilygeroudis, Loannis ^a   Prentzas, Jim ^b  

^a UNIVERSITY OF PATRAS   (Greece)

^b University of Patras and Computer Technology Institute   (Greece)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED INSTRUCTION; KNOWLEDGE REPRESENTATION; LIFE CYCLE; TEACHING;

DOMAIN KNOWLEDGE; HYBRID APPROACH; HYBRID FORMALISM; INTELLIGENT TUTORING SYSTEM; MULTI-PARADIGM; OPERATION AND MAINTENANCE; PEDAGOGICAL MODEL; USER MODELING;

INTELLIGENT VEHICLE HIGHWAY SYSTEMS;

EID: 33646026943     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-30139-4_9     Document Type: Article

References (10)

1. Gallant, S.I.: Neural Network Learning and Expert Systems. MIT Press (1993).
2. Golding, A.R., Rosenbloom, P.S.: Improving accuracy by combining rule-based and casebased reasoning. Artificial Intelligence 87 (1996) 215-254.
3. Guin-Duclosson, N., Jean-Danbias, S., Norgy, S.: The AMBRE ILE: How to Use CaseBased Reasoning to Teach Methods. In Cerri, S.A., Gouarderes, G., Paraguacu, F. (eds.): Sixth International Conference on Intelligent Tutoring Systems. Lecture Notes in Computer Science, Vol. 2363. Springer-Verlag, Berlin (2002) 782-791.
4. Hatzilygeroudis, I., Prentzas, J.: Neurules: Improving the Performance of Symbolic Rules. International Journal on Artificial Intelligence Tools 9 (2000) 113-130.
5. 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 (2004) 477-492.
6. Magoulas, G.D., Papanikolaou, K.A., Grigoriadou, M.: Neuro-fuzzy Synergism for Planning the Content in a Web-based Course. Informatica 25 (2001) 39-48.
7. 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 (1999).
8. Prentzas, J., Hatzilygeroudis, I., Garofalakis J.: A Web-Based Intelligent Tutoring System Using Hybrid Rules as its Representational Basis. In Cerri, S.A., Gouarderes, G., Paraguacu, F. (eds.): Sixth International Conference on Intelligent Tutoring Systems. Lecture Notes in Computer Science, Vol. 2363. Springer-Verlag, Berlin (2002) 119-128.
9. Vanlehn, K., Zhendong, N.: Bayesian student modeling, user interfaces and feedback: a sensitivity analysis. International Journal of AI in Education 12 (2001) 155-184.
10. Vassileva, J.: Dynamic Course Generation on the WWW. British Journal of Educational Technologies 29 (1998) 5-14.