Researches On Application Of Data Mining Technology In The Individual Network Teaching Platform

"Data driven the school, analysis transformation the education" is coming. It becomes the future development trend of education for teaching decision of using mining technology and learning analysis techniques to build a model of education. In education field, it is necessary to explore the correlation between variables of education for providing effective support. The emergence of "big data" will set off a human teaching and learning revolution. A lot of teaching data of the traditional learning in network and huge resources and did not play its due role truly in the education practice. In the analysis of traditional methods, it was mostly simple to query. We did little effect to the students as the main body of learning style factors to do deep analysis for implied information.Data mining technology can get useful knowledge from the mass in formation. It can make conducive decision to help managers. So the analysis of the application of data mining technology to the teaching information, will have a more important practical significance and meaning. In this paper, the main work is as follows:Firstly, the research and related theory on technology of data mining will lay foundation for the later chapters of the application.Secondly, the research and related theory on personalized network platform construction, will lay the foundation for the later chapters of the application.Thirdly, we introduced the application of data mining in the analysis of student course achievement. In view of the relationship between courses network platform setting and course grades, we mainly introduced shortcoming of existing performance analysis. Then we applied the association rules mining technology to the analysis of students’ achievement to find meaningful information hidden in the courses, thus to get valuable knowledge analysis, internal factors to find out all aspects of teaching effect of gains and losses and the impact of student achievement, so as to provide decision support for the selection of students and teachers in teaching and teaching management etc..At last, we introduced the application of the data mining technology in the personalized recommendation of curriculum knowledge. We proposed an improved a differential evolution algorithm(IDECluster) algorithm to solve the clustering problem. The clustering criterion function of the new algorithm is optimized, in order to obtain the optimal initial clustering center. We used this algorithm combining collaborative recommendation technology to analyze the learning content. According to the study interest on the course to different degrees, we divided the learners into different clusters, by cluster analysis. We then looked for the maximal similarity with the target learner in same cluster, to realize course recommendation on the level of interest of different courses of target learners. It is conducive useful to the realization of learning the personalized learning, to facilitate user navigation.We used the improved association rule algorithm to achieve mining access logs, find the knowledge point of webpage access frequency and difficult students’ interest and knowledge. To further dig out the access sequence between a number of knowledge points, We adapted these rules to adjust webpage content according to students view frequent sequences,better to provide users with personalized service.