The Study On The Personalized Teaching Platform Under E-learning Environment Based On Web Mining

The unprecedented abundance of instructional materials and flexibility has been provided to the users with the development and application of E-learning. The utilization rate of resources can be improved tremendously, and the teaching activities are free of the restriction of time, space and area,and also the users can learn independently because multiple users can visit the teaching platform running on servers simultaneously. However, most E-learning platforms based on Web have still centered on Website now and can not recommend the instructional resources according to the individual character. How to design a personalized E-learning teaching platform has gradually become a hot issue focused on by the researchers in many fields.In the paper, the whole study is based on the technology of Web mining,the user's browsing behaviors and browsing content under the E-learning environment are analyzed, a new mixed mode based on Vector Space Model is designed. The user's interests are represented by the two degree tree-model, the model can be updated according to the user's visiting times, browsing speed and resident time. Besides, a personalized model of teaching recommendation is designed which can mine the Web logs and interactive data. The process of exacting the access transaction set from Web logs is analyzed, and frequent itemsets are produced.Moreover, a new personalized recommendation algorithm based on frequent itemsets is designed. Finally, a personalized E-learning teaching platform is designed and the prototype system is implemented by applying the model and algorithm demonstrated in the paper.The experiment indicates that this system can provide the corresponding personalized service according to the users'interest and learning schedule.The personalized teaching system under E-learning environment involves broad fields. The users'interest model and personalized recommendation designed in the paper are only a part of the personalized teaching system, and the prototype system has been far from the actual application which is the next research content.