Research And Design Of Online Assisted Learning System Supporting Personalized Learning Resources

Online learning system (E-learning) is an important tool of many educational institutions and training institutions for training students. At present, many colleges and universities set up online learning system, and applied to the students in the teaching activities, along with the increasing of online learning in practice and application of system and auxiliary system of teaching resources, students often need to spend a lot of time and energy to find their needed resources in online learning system, even to spend a lot of time and effort that cannot find the required teaching resources, which makes the students’ learning efficiency decline even make students give up the online learning system. Through the analysis of the auxiliary system, current learning online we found the existing learning resources, a push to have the following several aspects:the keyword query, new resource recommendation, recommendation TOP-N. In recent years also appeared to recommend personalized recommendation algorithm based learning resources, but its focus is on if solve "information overload" and "lack of emotion" and how to meet the mobile learning problems. The above method can solve Learning Resource Recommendation problems to a certain extent, but is not recommended to solve the problem of personalized learning resources at all, so how in the online learning system for Personalized Learning Resource Recommendation has become a major problem in E-learning research.Based on the question, this paper first carries on the recommendation that algorithm is popular in the field of e-commerce personalized analysis summary, the pros and cons of each algorithm, and then the collaborative filtering recommendation algorithm were analyzed using. In order to solve the cold start problem and the sparsity problem in collaborative filtering algorithm, the system designed in this paper with recommendation algorithm content personalization recommendation based on, to make up for deficiencies bring collaborative filtering algorithm..At the same time, using the implicit and explicit score by way of the learning resources score, implicit rating can hide the student evaluation of the learning resources which are converted to the corresponding score, more suitable for online learning system, and increase the user’s score rate.Finally, online learning system based on the above ideas designed and implemented to support Personalized Learning Resource Recommendation, the system integrated into the ELKOE platform of Shaanxi Normal University, with teaching and application in software engineering.The personalized recommendation algorithm is introduced to the online learning system, as the higher education and training institutions to provide a broader platform, and also contribute to the research of personalized recommendation and optimization algorithm, provides the direction and thinking for the algorithm research.