Probabilistic and stochastic models such as Bayesian and Hidden Markov models can cope well with system uncertainties, but there is a problem of how learning state prediction and learning path generation are performed independently and how to connect them, and the overall effect of the system may be lost even after the connection. Using a Markov Decision Process, a kind of reinforcement learning model, not only can the prediction of the learning state of a student and the generation of a path be implemented simultaneously in a single model, but also the overall error can be reduced. In this paper, we propose to build an intelligent tutoring system into a Markov Decision Process model, an reinforcement learning model, with the aim of reducing learning path generation error and improving system performance by using Markov decision Process model in intelligent tutoring system. In addition, we propose a learning state evaluation method using a Markov Decision Process model to simultaneously proceed the student’s learning state estimation and the system’s action selection. We also propose a method to apply the value-iteration algorithm to action selection computation in a Markov Decision Process model. Comparison with previous models was carried out and its effectiveness was verified.
Kwon et al. (Fri,) studied this question.