Application Research Of Teaching Behavior Analysis Based On Virtual Learning Community

In the 21 st century, because education information policy widely is implemented,education data mining become a topic research,break limitations of time and space of the traditional education, apply data mining technology to the teaching system,mining valuable and meaningful information from the vast amounts of teaching data to serve the teaching and optimize teaching. In the network education platform of an asynchronous education way as the main characteristics, virtual learning community is widely used. In virtual learning community platform with a large number of underlying resources, add education data mining as a key technical support to find hidden valuable information. After adding education data mining technology, can be more intuitive to focus on individual differences among learners, pay attention to the collection and utilization of teaching resources, and provide the scientific basis for the decision of teaching, and make hidden rules and patterns visual. Make countless and irregular education information fragments into value teaching information, make efforts to create a suitable learning environment for every learner, promote the effectiveness and motivation of personalized learning, improve the enrichment and the globalization of teaching resources, and above content is the goal of education information.Based on full understanding the theory of virtual learning community and education data mining, select the virtual learning community platform as the source of research data. Use statistical analysis method and visual representation method to analyze the overall situation, the time distribution, so as to optimize resource allocation, adjust teaching important points and teaching time distribution;use hierarchical clustering method to analysis the interactive behavior between teachers and students, in order to discuss the interactive behavior characteristics in the process of online teaching. Make users classify hierarchically, dig up the opinion leaders and the lazy learners, make full use of the positive role of opinion leaders, to optimize the process of interaction in virtual learning community; construct knowledge point model using relational model, construct student model according to the students’ interest characteristics, combine collaborative filtering recommendation algorithm,use improved k-means method to analyze students’ learning interest characteristics,find the students’ uninterested knowledge points, and push the relevant coursechapters, learning resources and test cases to the student according to the knowledge point model, so as to meet the demand of students’ personalized learning of uninterested knowledge points. Eventually improve the teachers’ teaching quality,students’ learning enthusiasm and learning efficiency, achieve the goal of optimization of teaching.