Research on Feature Extraction and Clustering of Virtual Learning Community Network Learning

Virtual learning community is a kind of learning environment based on network is a new type of learning organization. However, Virtual Learning Community in the teaching data is often messy, fragmentary, it's value is often difficult to be detected and reasonable to use data mining techniques to deal with data will give us a analysis to study the effect of get twice the result with half the effort. Personalized teaching has always been a difficult point in the network teaching research, and the extraction and analysis of the characteristics of the learner's learning is the basis of personalized teaching. The purpose of this paper is through the acquisition learning in virtual learning community of learning behavior record and analysis learners learning characteristics, through the establishment of learning feature vectors, using fuzzy c-means algorithm for mining learners common characteristics. The common characteristics of individual learners as a research object of the transformation to the learner that clustering center feature vector as the research object, so that learning features simplified. Characteristics similar to the learner to divide into a group, formulated by the relevant experts in the field of practical teaching strategies and learning tasks, for each group are for teaching, personalized teaching, so as to improve the learning efficiency. Development of individualized instruction.