Cognitive Diagnosis (CD) aims to infer students’ latent knowledge states by analyzing their historical response logs. However, a limitation of most existing models is cognitive state blurring, as they tend to overlook the heterogeneous diagnostic signals embedded in different response behaviors and aggregate all interactions indiscriminately. This monolithic modeling approach makes it difficult to distinguish between students with similar overall proficiency but different specific weaknesses, leading to representations with low differentiability. Therefore, we propose a Relation-Aware Graph for Cognitive Diagnosis (RAGCD) framework to obtain more discriminative representations and enhance diagnostic accuracy. Specifically, our model first constructs a Student Cognitive Interaction Graph that decomposes interactions into a right-response subgraph and a wrong-response subgraph to capture distinct signals of mastery and misconception, respectively. Furthermore, we design a Heterogeneous View Attention mechanism to adaptively fuse the information from these different relational views. Finally, the fused representations are used to predict student performance, thus diagnosing their knowledge states more precisely. Extensive experiments on three real-world datasets show that RAGCD outperforms baseline models, indicating the effectiveness of our relation-aware approach.