@inproceedings{
yang2024disengcd,
title={Disen{GCD}: A Meta Multigraph-assisted Disentangled Graph Learning Framework for  Cognitive Diagnosis},
author={Shangshang Yang and Mingyang Chen and Ziwen Wang and Xiaoshan Yu and Panpan Zhang and Haiping Ma and Xingyi Zhang},
booktitle={The Thirty-eighth Annual Conference on Neural Information Processing Systems},
year={2024},
url={https://openreview.net/forum?id=lJuQxkDbDo}
}
Existing graph learning-based cognitive diagnosis (CD) methods have made relatively good results, but their student, exercise, and concept representations are learned and exchanged in an implicit unified graph, which makes the interaction-agnostic exercise and concept representations be learned poorly, failing to provide high robustness against noise in students' interactions. Besides,  lower-order exercise latent representations obtained in shallow layers are not well explored when learning the student representation. 
To tackle the issues, this paper suggests a meta multigraph-assisted disentangled graph learning framework for CD (DisenGCD), which learns three types of representations on three disentangled graphs: student-exercise-concept interaction,  exercise-concept relation, and concept dependency graphs, respectively. 
Specifically,  the latter two graphs are first disentangled from the interaction graph. 
Then, the student representation is learned from the interaction graph by a devised meta multigraph learning module; multiple learnable propagation paths in this module  enable current student latent representation to access  lower-order exercise latent representations,
which can lead to  more effective nad robust student representations learned; 
the exercise and concept representations are learned on the relation and dependency graphs by graph attention modules. 
Finally, a novel diagnostic function is devised to handle three disentangled representations for prediction.  Experiments show better performance and robustness of DisenGCD than state-of-the-art CD methods and demonstrate the effectiveness of the disentangled learning framework and meta multigraph module.The source code is available at https://github.com/BIMK/Intelligent-Education/tree/main/DisenGCD.
DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for  Cognitive Diagnosis
Shangshang Yang Mingyang Chen Ziwen Wang Xiaoshan Yu Panpan Zhang Haiping Ma Xingyi Zhang