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Massive Open Online Courses (MOOCs) which enable large-scale open online learning for massive users have been playing an important role in modern education for both students as well as professionals. To keep users' interest in MOOCs, recommender systems have been studied and deployed to recommend courses or videos that a user might be interested in. However, recommending courses and videos which usually cover a wide range of knowledge concepts does not consider user interests or learning needs regarding some specific concepts. This paper focuses on the task of recommending knowledge concepts of interest to users, which is challenging due to the sparsity of user-concept interactions given a large number of concepts. In this paper, we propose an approach by modeling information on MOOC platforms (e.g., teacher, video, course, and school) as a Heterogeneous Information Network (HIN) to learn user and concept representations using Graph Convolutional Networks based on user-user and