TY - GEN
T1 - Do micro-level tutorial decisions matter
T2 - 10th International Conference on Intelligent Tutoring Systems, ITS 2010
AU - Chi, Min
AU - VanLehn, Kurt
AU - Litman, Diane
N1 - Copyright:
Copyright 2011 Elsevier B.V., All rights reserved.
PY - 2010
Y1 - 2010
N2 - 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.
AB - 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.
KW - Human learning
KW - Intelligent tutoring systems
KW - Pedagogical strategy
KW - Reinforcement learning
UR - http://www.scopus.com/inward/record.url?scp=79957490177&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-13388-6_27
DO - 10.1007/978-3-642-13388-6_27
M3 - Conference contribution
AN - SCOPUS:79957490177
SN - 3642133878
SN - 9783642133879
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 224
EP - 234
BT - Intelligent Tutoring Systems - 10th International Conference, ITS 2010, Proceedings
Y2 - 14 June 2010 through 18 June 2010
ER -