Course recommender systems can help students identify the courses that are suitable or interesting to them. Existing course recommender systems prioritize accuracy, neglecting the crucial dimension of whether students trust the system's recommendations. To address this limitation, we posit that furnishing explanations to students can enhance their trust in the system. In this paper, we introduce a novel deep learning-based course recommendation model founded on a knowledge graph, which supports path visualization and empowers students to comprehend the rationales behind the recommendations. Specifically, we replace the hidden layer in the neural network with course features, which are subsequently trained to capture students' preferences for distinct features, informing the final recommendation based on these learned weightings.