Learning State Prediction and Learning Path Generation Using Hidden Markov Models

The main mission of the intelligent tutoring system is to estimate and predict the students’ learning status accurately and to generate a learning path that is inherent to the students. There are many learning state estimation and prediction methods, and there are also many methods to generate learning paths using the results. We propose a method to predict the learning state of a student and generate a learning path using the Hidden Markov Model, a probabilistic reasoning over time. We propose and illustrate in detail how to formalize the Hidden Markov Model using an extended knowledge graph for learning state prediction, how to collect the evidence variable values of the Hidden Markov Model in intelligent tutoring systems, how to predict the learning state using smoothing algorithms, and how to generate the stochastic learning path. The experiment was conducted to compare students using an intelligent tutoring system with those who did not. In addition, a comparison experiment of a method using Hidden Markov model with a multi-dimensional linear prediction model such as PFM and AFM was also carried out. The results showed that the average performance of students using the intelligent tutoring system was much higher than that of students without the intelligent tutoring system. In addition, the experimental results showed that the average performance of students using Hidden Markov models was higher than that of students using other models.