The Undergraduate-Oriented Framework of MOOCs Recommender System

Abstract

One of the challenges of MOOCs is to recommend appropriate courses to match individual characteristics of a special learner, although MOOCs are an excellent supplement of traditional higher education. Based on the effects of individual characteristics on learning, a novel framework is proposed for designing an undergraduate-oriented recommender system of MOOCs in which the particular characteristics of the participants, such as cognitive level, knowledge background, personal expectation, learning interest, learning motivation and learning style are emphasized. More specifically, the particular characteristics of undergraduates are introduced into the discussion of the recommendation methods including contentbased recommendation and collaborative filtering recommendation. Meanwhile, the recommendation strategies and recommendation algorithms are analyzed in the discussion.