A proposal for student modelling based on ontologies

Frontiers in Artificial Intelligence and Applications

Volumn 166, Issue , 2008, Pages 298-305

  De Antonio, Angélica ^a   Ramírez, Jaime ^a   Clemente, Julia ^b  

^a UNIVERSIDAD POLITÉCNICA DE MADRID   (Spain)

^b UNIVERSITY OF ALCALÁ   (Spain)

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER AIDED INSTRUCTION; EDUCATION COMPUTING; INTELLIGENT VEHICLE HIGHWAY SYSTEMS; ONTOLOGY; STUDENTS;

HIGH COMPLEXITY; INTELLIGENT TUTORING; MODEL-BASED OPC; NEW APPROACHES; ONTOLOGY'S; REASONING PROCESS; STUDENT KNOWLEDGE; STUDENT MODELING; STUDENT REASONING; TUTORING SYSTEM;

KNOWLEDGE REPRESENTATION;

EID: 84875958330     PISSN: 09226389     EISSN: 18798314     Source Type: Book Series    
DOI: None     Document Type: Conference Paper

References (26)

1. Mizoguchi, R. and Bordeau, J. Using ontological engineering to overcome common AI-ED problems. International Journal of Artificial Intelligence in Education, Vol. 11.
2. Petrushin, V. A. Intelligent Tutoring Systems: Architecture and Methods of Implementation. Journal of Computer and Systems Sciences International, Vol. 33, No.1, pp. 117-139, 1995.
3. Holt, P., Dubs S., Jones, M. and Greer, J. The State of Student Modelling. Student Modelling: The Key to Individualized Knowledge-Based Instruction. Springer-Verlag, pp. 3-39, 1994.
4. Clancey, W. J. GUIDON. Journal of Computer-Based Instruction, vol. 10, No. 1, pp. 8-14, 1983.
5. Carr, B. y Coldstein, I. Overlays: a theory of modelling for computer-aided instruction. International Journal of Man-Machine Studies, 5, pp. 215-236, 1977.
6. Holt, P., Dubs S., Jones, M. and Greer, J. The State of Student Modelling. Student Modelling: The Key to Individualized Knowledge-Based Instruction. Springer-Verlag, pp. 3-39, 1994.
7. Burton, R. R. y Brown, J.s. A tutoring and student modeling paradigm for gaming environments. ACM SIGCSE Bulletin, vol. 8, 1, pp. 236-246, 1978.
8. Clancey, W. J. Qualitative student models. Annual Review of computer Science, J. F. Traub, editor, 1, pp. 381-450, 1986.
9. Burton, R. R. Diagnosing bugs in a simple procedural skill. Intelligent tutoring systems. Ed. Sleeman, D. H, pp. 157-184, Academic Press, 1982.
10. Clancey, W. J. Qualitative student models. Annual Review of computer Science, J. F. Traub, ed, Vol. 1, pp. 381-450, 1986.
11. Kass, R. Student Modelling in intelligent tutoring systems-implictions for user modelling. User Models in Dialog Systems, eds. A. Kobsa y W. Wahlster, pp. 386-410, Berlin: Springer-Verlag, 1989.
12. Martin, B. Constraint-Based Modeling: Representing Student Knowledge. New Zealand Journal of Computing, vol. 7, No. 2, pp. 30-38, 1999.
13. Katz, S. and Lesgold, A. Modelling the student in SHERLOCK II. Student Modelling: the Key to Individualized Knowledge-Based Instruction, eds. Greer, J. E. and McCalla, G., vol. 125, pp. 99-125, Berling Heidelberg: Springer-Verlag, 1994.
14. Martin, Brent. Constraint-Based Modeling: Representing Student Knowledge. New Zealand Journal of Computing, vol. 7, 2, pp. 30-38, 1999.
15. Dillenbourg, P. and Self, J. A framework for learner modelling. Interactive Learning Environments, Vol. 2, No. 2, pp. 111-137, 1992.
16. Baffes, P. T. and Mooney R. Using theory revision to Model Students and Acquire Stereotypicar Errors. Proceedings of the 14 th Annual Conference of the Cognitive Science Society, pp. 617-622, Bloomington, 1992.
17. Koning, K. and Bredeweg, B. Using GDE in Educational Systems. Proceedings of the twelf International Workshop on qualitative reasoning, pp. 42-49, 1998
18. Wielinga, B. J. Schreiber, A. Th. and Breuker, J. A. KADS: A modelling approach to knowledge engineering, vol. 4, No. 1, pp. 5-53, 1992.
19. Derry, S.J. Metacognitive models of learning and instructional systems design. Adaptative Learning Environments: Foundations and Frontiers, eds. Jones, M. y Winne P., NATO ASI Series F, Berling Springer-Verlag, Vol. 85, pp. 257-286, 1992.
20. McCalla, G.I. and Greer, J.E. Granularity-based reasoning and belief revision in student models. Student Modelling: The key to Individualized Knowledge-based instruction, eds. Greer, J.E. and McCalla, G.I, Springer-Verlag, pp. 39-62, 1994
21. Del Soldato, T. Detecting and reacting to the learner's motivational state. Proceedings of the 2nd International Conference on Intelligent Tutoring Systems, Montreal, Quebec, eds. Frassson, C., Gauthier, G. y McCalla, G., Lecture Notes in Computer Science, Vol. 608, pp. 567-574, 1992
22. Chen, W. and Mizoguchi, R. Learner Model Ontology and Learner Model Agent. Cognitive Support for Learning-Imagining the Unknown. Eds. P. Kommers, IOS Press, pp. 189-200, 2004.
23. Knublauch, H., Fergerson, R. W., Noy, N. F. and Musen, M. A. The Protégé OWL Plugin: An Open Development Environment for Semantic Web Applications. Lecture Notes in Computer Science, Vol. 3298, pp. 229-243, 2004.
24. Krathwohl, D. Taxonomy of educational objectives: The classification of educational goals: Handbook 2: Affective domain. New York: Longman, Inc.
25. Bloom, B. S. Taxonomy of educational objectives. Published by Allyn and Bacon, Boston, MA. Pearson Education.
26. Harrow, A. J. A taxonomy of the psychomotor domain: a guide for developing behavioural objectives. Ed. Longman, 1972.