Research on Content-based MOOC Recommender Model
Ran Huang, Ran Lu · 2018
MOOC platform provides favorable conditions for people's lifelong learning and personalized learning. With the developments of MOOC, the supply of course resources will increase year by year and information overloading will become increasingly highlighted. At present, the major MOOC platforms provide only the classification and search functions of the courses, which are not enough. How to realize the innovative supply of courses resources and help the learners to locate the target course quickly, realizing the individualized learning, is also a question to be considered in the process of building intelligent MOOC platform. This paper, taking the platform of “iCourse”for example, proposed a content-based course recommender model for MOOC. The experiments show that the prediction precision of the proposed recommendation model is much higher than that of random recommendation and the more exact and comprehensive the descriptive data of course is, the higher the recommendation precision of the model proposed is, proving the validity of the content-based MOOC recommender model.