The Research Of The Learners’ Social Interaction Characteristics And Interactive Strategy In Virtual Learning Community

With the development of computer network technology and the concept of distance education, various types of virtual learning community have sprung up rapid rise, the characteristics of its open, flexible are attracted by the education practitioners and learners of all ages, It meets the needs of the learners’ interpersonal through the network and breaks the shackles of the traditional teaching concept, makes learning happen in different time and space, it also implements the supplement of classroom teaching and provides learners with a guide way of learning. While, in the present teaching practice of virtual learning community, the learners still have positive social interaction problems such as low frequency, shallow interactive content. Research of the learners’ social interaction characteristics and interaction strategy can effectively improve the learners’ social interaction enthusiasm and initiative, and provide reference and reference for teaching practitioners which can organize activities of interaction and solve the problem of interaction effectively.Learning analysis is a hot research topic in the field of education technology recently, it breaks through the previous research which extract data cycle takes longer, and costs more manpower and limitations, it provides a new perspective of virtual learning community social interaction study in the big data age, This study will do the research of learners’ social interaction characteristics in virtual learning community with the technical support of learning analysis. Analyze the social interactive data in the course of the first phase with using SNAPP and UCINET tools, the analysis from three views of macroscopic, the middle, microcosmic to find learners’ social network structure characteristics and four different types of learners in virtual learning community. Then use the CMC content analysis method, which based on the new bloom cognitive process dimensions from six major categories and 19 categories of learners to the deep analysis of the social interaction to find the learners’ cognitive level. Finally use the interview method to interview learners on the edge of social networks, try to understand the causes of the negative interactions. In the second course stage put forward and implement the interactive strategy which mainly accorded to the preliminary data analysis results, by analyzing the related data of social interaction in this stage, the change of the social network structure and cognitive characteristics can verify the validity of the proposed interaction strategy, which can effectively organize social interaction activities, and provide the reference and basis of improving the quality of social interaction.