Abstract
Intelligent tutoring has started to, and will play an important role in education and training. A challenging task in building an intelligent tutoring system (ITS) is to create and maintain an optimal teaching strategy. In this paper, we present a new technique for addressing this challenge. We cast an intelligent tutoring system as a Markov decision process (MDP), and apply a reinforcement learning (RL) algorithm to learn the optimal teaching strategy through interactions between the system and students. This technique enables the system to teach a student based on his/her studying states, and allows the system to learn the optimal teaching strategy in an online fashion.
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Wang, F. (2014). Learning Teaching in Teaching: Online Reinforcement Learning for Intelligent Tutoring. In: Park, J., Stojmenovic, I., Choi, M., Xhafa, F. (eds) Future Information Technology. Lecture Notes in Electrical Engineering, vol 276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40861-8_29
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DOI: https://doi.org/10.1007/978-3-642-40861-8_29
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-40860-1
Online ISBN: 978-3-642-40861-8
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