Leveraging a domain ontology to increase the quality of feedback in an intelligent tutoring system

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

Volumn 6094 LNCS, Issue PART 1, 2010, Pages 75-84

  Kazi, Hameedullah ^a,c   Haddawy, Peter ^a   Suebnukarn, Siriwan ^b  

^a ASIAN INSTITUTE OF TECHNOLOGY   (Thailand)

^b THAMMASAT UNIVERSITY   (Thailand)

^c ISRA UNIVERSITY   (Pakistan)

Author keywords

Hint generation; Intelligent tutoring systems; Knowledge acquisition bottleneck; Medical PBL; Ontology; Student model; UMLS

Indexed keywords

HINT GENERATION; INTELLIGENT TUTORING SYSTEM; MEDICAL PBL; STUDENT MODEL; UMLS;

COMPUTER AIDED INSTRUCTION; EDUCATION COMPUTING; KNOWLEDGE ACQUISITION; STUDENTS;

ONTOLOGY;

EID: 79957513876     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-13388-6_12     Document Type: Conference Paper

References (21)

1. Crowley, R., Medvedeva, O.: An Intelligent Tutoring System for Visual Classification Problem Solving. Artificial Intelligence in Medicine 36(1), 85-117 (2006). (Pubitemid 43068585)
2. Day, M.Y., Lu, C., Yang, J.D., Chiou, G., Ong, C.S., Hsu, W.: Designing an Ontology- Based Intelligent Tutoring Agent with Instant Messaging. In: Fifth IEEE International Conference on Advanced Learning Technologies, pp. 318-320 (2005). (Pubitemid 44459384)
3. Mills, B., Evens, M., Freedman, R.: Implementing directed lines of reasoning in an intelligent tutoring system using the atlas planning environment. In: International Conference on Information Technology, pp. 729-733 (2004).
4. Suraweera, P., Mitrovic, A., Martin, B.: A Knowledge Acquisition System for Constraint Based Intelligent Tutoring Systems. In: Conference on AI in Education, pp. 638-645 (2005).
5. Anderson, J.R., Corbett, A., Koedinger, K., Pelletier, R.: Cognitive tutors: Lessons learned. Journal of the Learning Sciences 4(2), 167-207 (1996).
6. Mitrovic, A.: Experiences in implementing constraint-based modelling in SQL-Tutor. In: Goettl, B.P., Halff, H.M., Redfield, C.L., Shute, V.J. (eds.) ITS 1998. LNCS, vol. 1452, pp. 414-423. Springer, Heidelberg (1998).
7. Self, J.A.: Bypassing, the Intractable Problem of Student Modelling. In: Proceedings of ITS 1988, Montreal, pp. 18-24 (1988).
8. VanLehn, K., Lynch, C., Schulze, K., Shapiro, J.A., Shelby, R., Taylor, L., Treacy, D., Weinstein, A., Wintersgill, M.: The Andes Physics Tutoring System: Lessons Learned. International Journal of Artificial Intelligence in Education 15, 147-204 (2005).
9. Murray, T.: Expanding the knowledge acquisition bottleneck for intelligent tutoring systems. International Journal of Artificial Intelligence in Education 8, 222-232 (1997).
10. Kodaganallur, V., Weitz, R.R., Rosenthal, D.: A Comparison of Model-Tracing and Constraint- Based Intelligent Tutoring Paradigms. International Journal of Artificial Intelligence in Education 152, 117-144 (2005).
11. Mitrovic, A., Koedinger, K.R., Martin, B.: A Comparative Analysis of Cognitive Tutoring and Constraint-Based Modeling. In: Brusilovsky, P., Corbett, A.T., de Rosis, F. (eds.) UM 2003. LNCS, vol. 2702, pp. 313-322. Springer, Heidelberg (2003). (Pubitemid 36768581)
12. Martin, B., Mitrovic, A.: Easing the ITS Bottleneck with Constraint-Based Modelling. New Zealand Journal of Computing 8(3), 38-47 (2001).
13. Pople Jr., H.E.: Heuristic Methods for Imposing Structure on Ill-Structured Problems: The Structuring of Medical Diagnostics. In: Szolovits, P. (ed.) Artificial Intelligence in Medicine, ch. 5. Westview Press, Boulder (1982).
14. Kazi, H., Haddawy, P., Suebnukarn, S.: Expanding the Space of Plausible Solutions in a Medical Tutoring System for Problem Based Learning. International Journal of Artificial Intelligence in Education (in Press).
15. Suebnukarn, S., Haddawy, P.: Modeling Individual and Collaborative Problem-Solving in Medical Problem-Based Learning. User Modeling and User-Adapted Interaction 16(3-4), 211-248 (2006). (Pubitemid 44484492)
16. U.S. National Library of Medicine, http://www.nlm.nih.gov/research/umls/.
17. Kazi, H., Haddawy, P., Suebnukarn, S.: Expanding the Space of Plausible Solutions For Robustness in an Intelligent Tutoring System. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds.) ITS 2008. LNCS, vol. 5091, pp. 583-592. Springer, Heidelberg (2008).
18. Fiedler, A., Tsovaltzi, D.: Domain-Knowledge Manipulation for Dialogue-Adaptive Hinting. In: Proceedings of the 12th International Conference on Artificial Intelligence in Education (AIED 2005), pp. 801-803 (2005).
19. Crowley, R.S., Tseytlin, E., Jukic, D.: ReportTutor - an intelligent tutoring system that uses a natural language interface. In: Proc. AMIA Symp. 2005, pp. 171-175 (2005).
20. Feltovich, P.J., Barrows, H.S.: Issues of generality in medical problem solving. In: Schmidt, H.G., De Volder, M.L. (eds.) Tutorials in problem-based learning: A new direction in teaching the health professions. Van Gorcum, The Netherlands (1984).
21. Al-Mubaid, H., Nguyen, H.A.: A Cluster Based Approach for Semantic Similarity in the Biomedical Domain. In: Proceedings of the 28th IEEE EMBS Annual International Conference, New York, USA, August 30-September 3 (2006).