Research On Data Mining Of Students Classification In Distance Learning System

Along with the development of internet Technologies, distance learning grows rapidly. Due to its unique teaching mode, distance learning makes it possible for education institution to supply different learning strategy to students with different learning ability. However, as the general education, the existing distance learning supply the same subjects and teaching strategy to the students of different performance. There are a lot of theoretical works on data mining of distance learning,but the practical application is few. This paper tries to study distance learning mode, combine with the theory of education to assess students in distance education systems, in order to provide evidence to teachers who formulate strategy according to different types of students.Firstly, this paper describes the present condition of distance learning and data mining application in distance learning, and points out that it is research significance to classify students. Secondly, this paper builds a data mining model, which is supported by distance learning system. Teachers can design different teaching strategies for different kinds of students according to the classification and clustering result of students in teachers'operation platform. When students enter into login system, they can get corresponding learning strategy to study according to classification prediction. To summarize, this paper builds a systematic database, and introduces a method evaluating the student capacity based on fuzzy comprehensive evaluation, then saves the evaluation result as a new attribute in the database and takes as a main clustering attribute in the later prater of data mining analysis. As well, this paper applies the improved the STING arithmetic in student classification model, and have a case study.Finally, this paper assesses the model and explains the significance of mode founded in Clustering results, which has meaning of guidance for practical applications.