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Do micro-level tutorial decisions matter: Applying reinforcement learning to induce pedagogical tutorial tactics

Chi, M and Vanlehn, K and Litman, D (2010) Do micro-level tutorial decisions matter: Applying reinforcement learning to induce pedagogical tutorial tactics. In: UNSPECIFIED UNSPECIFIED, 224 - 234. ISBN 3642133878, 9783642133879

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Abstract

Pedagogical tutorial tactics are policies for a tutor to decide the next action when there are multiple actions available. When the contents were controlled so as to be the same, little evidence has shown that tutorial decisions would impact students' learning. In this paper, we applied Reinforcement Learning (RL) to induce two sets of tutorial tactics from pre-existing human interaction data. The NormGain set was derived with the goal of enhancing tutorial decisions that contribute to learning while the InvNormGain set was derived with the goal of enhancing those decisions that contribute less or even nothing to learning. The two sets were then compared with human students. Our results showed that when the contents were controlled so as to be the same, different pedagogical tutorial tactics would make a difference in learning and more specifically, the NormGain students outperformed their peers. © Springer-Verlag Berlin Heidelberg 2010.


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Details

Item Type: Book Section
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chi, M
Vanlehn, K
Litman, Ddlitman@pitt.eduDLITMAN
Centers: University Centers > Learning Research and Development Center (LRDC)
Date: 1 December 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: 6094 L
Number: PART 1
Page Range: 224 - 234
Event Type: Conference
DOI or Unique Handle: 10.1007/978-3-642-13388-6_27
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: 3642133878, 9783642133879
ISSN: 0302-9743
Date Deposited: 05 May 2015 16:25
Last Modified: 01 Aug 2019 13:59
URI: http://d-scholarship.pitt.edu/id/eprint/22932

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