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

Authors:
Min Chi;Kurt VanLehn;Diane Litman
Affiliations:
Machine Learning Department, Carnegie Mellon University, PA;School of Computing and Informatics, Arizona State University, AZ;Department of Computer Science & Learning Research Development Center, University of Pittsburgh, PA
Venue:
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Year:
2010

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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.