DisenGCD introduces a meta multigraph-assisted framework for cognitive diagnosis that addresses limitations in existing methods by disentangling student, exercise, and concept representations into three distinct graphs. This approach enhances the learning of student representations through effective access to lower-order exercise representations and improves robustness against noise in student interactions. Experimental results demonstrate that DisenGCD outperforms state-of-the-art methods in cognitive diagnosis tasks.