Abstract
We have proposed a novel application of Markov decision processes (MDPs), a reinforcement learning technique, to automatically generate hints for an intelligent tutor that learns. We demonstrate the feasibility of this approach by extracting MDPs from four semesters of student solutions in a logic proof tutor, and calculating the probability that we will be able to generate hints at any point in a given problem. Our results indicate that extracted MDPs and our proposed hint-generating functions will be able to provide hints over 80% of the time. Our results also indicate that we can provide valuable tradeoffs between hint specificity and the amount of data used to create an MDP.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
McKendree, J.: Effective Feedback Content for Tutoring Complex Skills. Human-Computer Interaction 5(4), 381–413 (1990)
Murray, T.: Authoring intelligent tutoring systems: An analysis of the state of the art. Intl. J. Artificial Intelligence in Education 10, 98–129 (1999)
Mitrovic, A., Koedinger, K., Martin, B.: A comparative analysis of cognitive tutoring and constraint-based modeling. User Modeling, 313–322 (2003)
Koedinger, K.R., Aleven, V., Heffernan., T., McLaren, B., Hockenberry, M.: Opening the door to non-programmers: Authoring intelligent tutor behavior by demonstration. In: 7th Intelligent Tutoring Systems Conference, Maceio, Brazil, pp. 162–173 (2004)
McLaren, B., Koedinger, K., Schneider, M., Harrer, A., Bollen, L.: Bootstrapping Novice Data: Semi-automated tutor authoring using student log files. In: Lester, J.C., Vicari, R.M., Paraguaçu, F. (eds.) ITS 2004. LNCS, vol. 3220. Springer, Heidelberg (2004)
Beck, J., Woolf, B.P., Beal, C.R.: ADVISOR: A Machine Learning Architecture for Intelligent Tutor Construction. In: 7th National Conference on Artificial intelligence, pp. 552–557. AAAI Press / The MIT Press (2000)
Merceron, A., Yacef, K.: Educational Data Mining: a Case Study. In: 12th Intl. Conf. on Artificial Intelligence in Education. IOS Press, Amsterdam (2005)
Barnes, T., Stamper, J.: Toward the extraction of production rules for solving logic proofs. In: Proc. 13th Intl. Conf. on Artificial Intelligence in Education, Educational Data Mining Workshop, Marina del Rey (2007)
Croy, M., Barnes, T., Stamper, J.: Towards an Intelligent Tutoring System for propositional proof construction. In: Brey, P., Briggle, A., Waelbers, K. (eds.) European Computing and Philosophy Conference. IOS Publishers, Amsterdam (2007)
Sutton, S., Barto, A.: Reinforcement Learning: An Introduction. MIT Press, Cambridge (1998)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Barnes, T., Stamper, J. (2008). Toward Automatic Hint Generation for Logic Proof Tutoring Using Historical Student Data. In: Woolf, B.P., Aïmeur, E., Nkambou, R., Lajoie, S. (eds) Intelligent Tutoring Systems. ITS 2008. Lecture Notes in Computer Science, vol 5091. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69132-7_41
Download citation
DOI: https://doi.org/10.1007/978-3-540-69132-7_41
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69130-3
Online ISBN: 978-3-540-69132-7
eBook Packages: Computer ScienceComputer Science (R0)