Design And Implementation Of Personalized Learning System Based On Web Data Mining Technology

With the rapid development of computer technology, network technology, as well as people’s needs constant depth, the traditional Web technology is no longer able to meet the requirements of the development of people’s personality. In traditional web learning mode, access to the user’s Web information are passive, all information received unified information, and can’t display Web information based on user characteristics, which leads the Web information useful to the user can not sent to the user, the user can also get less than what you want the Web to learn information. Push personalized learning content for different users, has become a new direction of demand, compared with the traditional teaching model, based on the environmental constraints of Web-based remote education regardless of time, the life-long education possible.In this thesis, both from the theoretical and technical aspects depth discussion of how to build a real network personalized learning support system. There is a lot of useful information in the Web-based distance education. For example, the Web log information can know the sequence of access to the user to access the page, the process of the IP address of the user to access the system, a certain period of time a system visits as well as a user accesses the system error log, etc.; addition, most online system requires a user registration system during use, which makes the system in the presence of a large number of registration and enrollment information. The information for the website reorganization of resources, personalized learning and behavior analysis has important significance. This article describes how to log preprocessing the formation of the session, and then the association rules Apriori algorithm to find frequent user access to the project set access rules, and the process to achieve the use of Web logs. At the same time, through the existing data sets of experiments, the effectiveness of the algorithm and set of operating parameters. For student enrollment data, calculated according to the student elective course similarity between the students, on this basis, the use of the existing complex network clustering techniques to cluster the students, and clustering The results were analyzed and discussed.Based Web network education has changed the traditional teaching mode, make learning more flexible, students can choose the learning content and learning, to make learning more efficient and targeted. For schools, this teaching model can optimize teaching resources, teaching through the network in real-time for the needs of students and student learning characteristics, and thus provide a basis to further explore new teaching.