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Inducing effective pedagogical strategies using learning context features

Chi, M and Vanlehn, K and Litman, D and Jordan, P (2010) Inducing effective pedagogical strategies using learning context features. In: UNSPECIFIED UNSPECIFIED, 147 - 158. ISBN 3642134696, 9783642134692

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Abstract

Effective pedagogical strategies are important for e-learning environments. While it is assumed that an effective learning environment should craft and adapt its actions to the user's needs, it is often not clear how to do so. In this paper, we used a Natural Language Tutoring System named Cordillera and applied Reinforcement Learning (RL) to induce pedagogical strategies directly from pre-existing human user interaction corpora. 50 features were explored to model the learning context. Of these features, domain-oriented and system performance features were the most influential while user performance and background features were rarely selected. The induced pedagogical strategies were then evaluated on real users and results were compared with pre-existing human user interaction corpora. Overall, our results show that RL is a feasible approach to induce effective, adaptive pedagogical strategies by using a relatively small training corpus. Moreover, we believe that our approach can be used to develop other adaptive and personalized learning environments. © 2010 Springer-Verlag.


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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chi, M
Vanlehn, K
Litman, Ddlitman@pitt.eduDLITMAN
Jordan, Ppjordan@pitt.eduPJORDAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 20 July 2010
Date Type: Publication
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Journal or Publication Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume: 6075 L
Page Range: 147 - 158
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-13470-8_15
Institution: University of Pittsburgh
Schools and Programs: Dietrich School of Arts and Sciences > Computer Science
Dietrich School of Arts and Sciences > Intelligent Systems
Refereed: Yes
ISBN: 3642134696, 9783642134692
ISSN: 0302-9743
Date Deposited: 05 May 2015 16:27
Last Modified: 03 Aug 2019 14:55
URI: http://d-scholarship.pitt.edu/id/eprint/22934

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