Research And Application Of User Credibility Based Personalization Recommendation In MOOC

As one of the most popular online learning methods, MOOC develops rapidly in recent years. A large number of MOOC platforms have emerged at home and abroad, and many researchers have also put their sights on the MOOC field, they studied on the characteristics of MOOC, the user components, the development prospect of MOOC and so on. The researchers found that MOOC has the characteristics of openness, diversity, autonomy and so on; The majority of MOOC users are young people; Users who participate in MOOC study have different learning objectives and earn differently. At this stage, MOOC is in a period of rapid growth, and it has great potential, even some researchers believe that MOOC will dominate the future of online education.MOOC began to attract people’s great attention since 2012, and it should also be in the exploratory phase. MOOC has a unique advantage, but also a lot of deficiencies. The main work of this paper is to solve how to evaluate MOOC and personalized recommendation, the main work is as follows:1. The evaluation mechanism of MOOCThis study found that the number of MOOC student is very much and demand is strong, but many people ignore the discussion on the evaluation mechanism of MOOC, Most MOOC platforms do not provide users with the way to evaluate the course, Users do not understand the quality of the course. In this paper, we put forward a multi-level scoring mechanism in the field of MOOC, system collects user rating data, shows user views and reflects the quality of the course.2. Personalized recommendation for MOOCCombined with user rating data, improved collaborative filtering algorithm is adopted in the text, namely recommendation algorithm based on user dependability. The collected user’s rating data were analyzed, identify similar users and recommend to users the most likely course of interest.3. Build a learning, rating and recommendation prototype systemSome MOOC resources are collected in this paper, and prototype verification system is constructed. Users can watch MOOC courses and score these courses in this system. Based on the collected user data, the system will recommend MOOC course to users.