Abstract
Cognitive modelling in intelligent tutoring systems aims at identifying a learner’s skills and knowledge from his answers to tutor questions and other observed behaviour. In this paper, we propose an innovative variant of Shapiro’s algorithmic debugging technique whose application can be used to pin-point learners’ erroneous behaviour in terms of an irreducible disagreement to the execution trace of an expert model. Our variant has two major benefits: in contrast to traditional approaches, it does not rely on an explicit encoding on mal-rules, and second, it induces a natural teacher-learner dialogue with no need for the prior scripting of individial turns or higher-level dialogue planning.
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
Beller, S., Hoppe, U.: Deductive error reconstruction and classification in a logic programming framework. In: Brna, P., Ohlsson, S., Pain, H. (eds.) Proc. of the World Conference on Artificial Intelligence in Education, pp. 433–440 (1993)
Brown, J.S., Burton, R.R.: Diagnostic models for procedural bugs in basic mathematical skills. Cognitive Science 2, 155–192 (1978)
Burton, R.R.: Debuggy: Diagnosis of errors in basic mathematical skills. In: Sherman, D., Brown, J.S. (eds.) Intelligent Tutoring Systems. Academic Press, London (1982)
Corbett, A.T., Anderson, J.R., Patterson, E.J.: Problem compilation and tutoring flexibility in the lisp tutor. In: International Conference of Intelligent Tutoring Systems, Montreal (1988)
Kawai, K., Mizoguchi, R., Kakusho, O., Toyoda, J.: A framework for ICAI systems based on inductive inference and logic programming. New Generation Computing 5, 115–129 (1987)
Koedinger, K.R., Anderson, J.R., Hadley, W.H., Mark, M.A.: Intelligent tutoring goes to school in the big city. Journal of Artificial Intelligence in Education 8(1), 30–43 (1997)
Reiser, B.J., Anderson, J.R., Farrell, R.G.: Dynamic student modelling in an intelligent tutor for lisp programming. In: IJCAI 1985: Proceedings of the 9th International Joint Conference on Artificial Intelligence, pp. 8–14. Morgan Kaufmann Publishers Inc., San Francisco (1985)
Self, J.: Student models and artificial intelligence. Computers & Education 3, 309–312 (1979)
Shapiro, E.Y.: Algorithmic Program Debugging. ACM Distinguished Dissertations. MIT Press (1983); Thesis (Ph.D.) – Yale University (1982)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2011 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zinn, C. (2011). Algorithmic Debugging to Support Cognitive Diagnosis in Tutoring Systems. In: Bach, J., Edelkamp, S. (eds) KI 2011: Advances in Artificial Intelligence. KI 2011. Lecture Notes in Computer Science(), vol 7006. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24455-1_35
Download citation
DOI: https://doi.org/10.1007/978-3-642-24455-1_35
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24454-4
Online ISBN: 978-3-642-24455-1
eBook Packages: Computer ScienceComputer Science (R0)