Study of the Learning Model Based on Improved ID3 Algorithm

Abstract

The network learning behavior intelligence analysis system can collect the information of learner's psychology, behavior, methods and effectiveness in the learning process, and classify learners by using the ID3 algorithm based on the internal factors and personality characteristics of learners that influence the learning effect. In order to correct the shortcomings that the ID3 algorithm more inclined to the attributes that have more values in the classification process, we introduce user interest, which used to distinguish the dependence between different information attributes. At the same time, we introduce parameters to reduce the redundancy between attributes, and accelerate the pace of information entropy reducing, then construct a general, expandable senior vocational student model in the intelligence-learning environment.