I. INTRODUCTION
How to use appropriate languages, appropriate methods and appropriate knowledge sizes to submit the right information to the right students at the right time frame is one of the three open problems in artificial intelligence presented by RAJ REDDY [1] in 2003.
In education fields, intelligent tutoring systems (ITSs) fulfill this purpose. The key feature of intelligent tutoring systems [2] is the ability to provide a user self-adapted presentation of the teaching material [3] by using artificial intelligence methods to represent the pedagogical decisions and the information about each user.
Hence, in intelligent tutoring systems, user knowledge management is necessary. That is, the buliding and updating of a user knowledge base is of great importance in order to increase the self-adaption of an intelligent tutoring system and to enhance pedagogical effects of the system.
The user knowledge base is constructed based on users’ basic information, knowledge levels, learning styles, psychology characteristics, etc. in order to improve the system’s self-adaptability and pedagogical effects.