Intelligent Tutoring System

The mam study in this paper is how to enhance the intelligence and universality of an intelligent tutoring system (ITS) First of all, improving the architecture of ITS. Secondly, on researching the knowledge representation's status, a new knowledge representation, i.e. XML knowledge representation based on object and semantic network, is put forward. Thirdly, by introducing fuzzy theory into system evaluation, evaluating student, teaching, course resource, and function of whole system. Fourthly, making use of Learning from examples based on information theory, machine learning algorithm is improved and machine learning decision tree is realized. Finally, on reasoning mechanism, combining means of two classes reasoning is taken. Namely the teaching sequence is made certain by the fuzzy reasoning based on semantic network, and the teaching method is established by the reasoning based on production rule. The paper introduces the detailed reasoning algorithm to both.