Data Mining Techniques In Network Teaching System

With the network penetrating into various fields, a Web-based network education is also flourishing more and more. As a certain supplement of the traditional central education, the network-based education has the superiority which cannot be substituted. People can study without the limitation of the time and location. But the present network-based educational system still has its deficiencies, it can't consider students'characteristics adequately to set the personalized teaching contents and teaching strategies. According to above insufficiencies, based on data mining technology and student models theory, the thesis has studied an intelligent network-based educational system. Association rules and clustering are used in the thesis. Association rules mining can mine valuable and data relationship knowledge from a lot of data. Clustering mining can divide a group of physical or abstract objects into several groups according to the similitude degree between them. Among them, the similar object constitutes a group.First, the thesis analyzes the development status and problems of domestic and foreign individualized network learning. Then the necessity of individualized network learning is obtained. Only undertake personalized teaching, it can better student services, improve their learning efficiency and stimulate the students'study interest, in order to achieve a better teaching effect. Next,it introduces relational knowledge of network education and data mining technology separately. Afterwards it designs student model in network education. In addition, it changes the model into six sub-models which contains knowledge model, cognitive model, media preference model, learning and emotional model, social characteristics model and emotional meaning dynamic model into specific. Finally, clustering technology and association rules algorithm are used to analyze students'feature by their level, ability to learn, learning preferences, interest and so on, in order to find useful learning pattern. So we can know different types with different knowledge needs. And we also can adjust corresponding teaching content and strategies. As a result, the system of personalized online education is able to improve its intelligence. Thus it can provide better targeted service for students. And it achieves goal of reasonable teaching really.