In Intelligent Tutoring System (ITS) as well as the E-learning system at the university, predicting student learning performance to suggest courses is an essential task of an academic advisor. Many kinds of research address to solve this problem with diverse approaches such as classification, regression, association rules, and recommender systems. Recently, it was a measurable success in using collaborative filtering in the recommender system, especially the matrix factorization technique, to build the courses' recommendation system. There are many advances to improve the accuracy of the prediction, such as using student profiles, course properties, or course relationships; however, they have not been mined. This study proposes an approach which integrates the course relationships into the courses' recommendation system to improve the prediction accuracy. Experimental results of the proposed approach are positive when we validate the published educational datasets.