With the rapid growth of massive online open courses (MOOCs) on the Web, it is essential to provide learners with appropriate assistance in courses and learning materials. The extant approaches of personalized course search mainly consider historical learnt and enrolled courses of learners. That is, those courses which are contently similar to previous courses in learner profiles will be highlighted in the ranking results of the personalized course search. However, these approaches mainly neglect two distinguished characteristics in this domain, which are (i) context-dependent: course search which is highly correlated with learner contexts, e.g., a learner may have the individual learning schedule of the courses to be retrieved depending on the temporal contexts; and (ii) knowledge-constrained: learners are more willing to search and enroll in the courses that they have sufficient pre-knowledge about. To incorporate these two domain characteristics of the personalized course search, we therefore present a novel approach based on hybrid learner profile in this paper. Furthermore, we conduct the experiments which compare the performance of different methods on a dataset to verify the effectiveness of the proposed method for the personalized course search.