Abstract
In this paper we explore the problem of selecting appropriate Knowledge Construction Dialogues (KCDs) for the purpose of encouraging students to include important points in their qualitative physics explanations that are missing. We describe a hybrid symbolic/statistical approach developed in the context of the Why2 conceptual physics tutor (Vanlehn et al., 2002). Our preliminary results demonstrate that our hybrid approach outperforms both the symbolic approach and the statistical approach by themselves.
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.
Similar content being viewed by others
References
V. Aleven, O. Popescu, and K. Koedinger. 2001. Pedagogical content knowledge in a tutorial dialogue system to support self-explanation. In Papers of the AIED-2001 Workshop on Tutorial Dialogue Systems.
C. Burgess, K. Livesay, and K. Lund. 1998. Explorations in context space: Words, sentences, discourse. Discourse Processes, 25(2):211–257.
M. Chi, N. de Leeuw, M. Chiu, and C. LaVancher. 1981. Eliciting self-exsplanations improves understanding. Cognitive Science, 18(3).
M. G. Core, J. D. Moore, and C. Zinn. 2001. Initiative management for tutorial dialogue. In Proceedings of the NAACL Workshop Adaption in Dialogue Systems.
M. S. Glass. 1999. Broadening Input Understanding in an Intelligent Tutoring System. Ph.D. thesis, Illinois Institute of Technology.
Pamela W. Jordan, Maxim Makatchev, Michael Ringenberg, and Kurt VanLehn. 2002. Engineering the Tacitus-lite weighted abductive inference engine for use in the Why-Atlas qualitative physics tutoring system. submitted.
W. Kintsch. 2002. Predication. to appear in the Cognitive Science Journal.
T. K. Landauer, P. W. Foltz, and D. Laham. 1998. Introduction to latent semantic analysis. To Appear in Discourse Processes.
Andrew Kachites McCallum. 1996. Bow: A toolkit for statistical language modeling, text retrieval, classification and clustering. http://www.cs.cmu.edu/mccallum/bow.
Mitchel, T. 1997. Machine Learning. McGraw Hill.
C. P. Rosé, P. Jordan, M. Ringenberg, S. Siler, K. VanLehn, and A. Weinstein. 2001. Interactive conceptual tutoring in atlas-andes. In Proceedings of Artificial Intelligence in Education.
C. P. Rosé. 2000. A framework for robust sentence level interpretation. In Proceedings of the First Meeting of the North American Chapter of the Association for Computational Lingusitics.
K. VanLehn, P. Jordan, C. P. Rosé, and The Natural Language Tutoring Group. 2002. The architecture of why2-atlas: a coach for qualitative physics essay writing. In Proceedings of the Intelligent Tutoring Conference.
P. Wiemer-Hastings and I. Zipitria. 2001. Rules for syntax, vectors for semantics. In Proceedings of the Twenty-third Annual Conference of the Cognitive Science Society.
P. Wiemer-Hastings, A. Graesser, D. Harter, and the Tutoring Research Group. 1998. The foundations and architecture of autotutor. In B. Goettl, H. Halff, C. Redfield, and V. Shute, editors, Intelligent Tutoring Systems: 4th International Conference (ITS’ 98), pages 334–343. Springer Verlag.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Rosé, C.P., Bhembe, D., Roque, A., Siler, S., Srivastava, R., VanLehn, K. (2002). A Hybrid Language Understanding Approach for Robust Selection of Tutoring Goals. In: Cerri, S.A., Gouardères, G., Paraguaçu, F. (eds) Intelligent Tutoring Systems. ITS 2002. Lecture Notes in Computer Science, vol 2363. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-47987-2_57
Download citation
DOI: https://doi.org/10.1007/3-540-47987-2_57
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-43750-5
Online ISBN: 978-3-540-47987-1
eBook Packages: Springer Book Archive