DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis

DGCD: An Adaptive Denoising GNN for Group-level Cognitive Diagnosis

Haiping Ma, Siyu Song, Chuan Qin, Xiaoshan Yu, Limiao Zhang, Xingyi Zhang, Hengshu Zhu

Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence
Main Track. Pages 2261-2269. https://doi.org/10.24963/ijcai.2024/250

Group-level cognitive diagnosis, pivotal in intelligent education, aims to effectively assess group-level knowledge proficiency by modeling the learning behaviors of individuals within the group. Existing methods typically conceptualize the group as an abstract entity or aggregate the knowledge levels of all members to represent the group’s overall ability. However, these methods neglect the high-order connectivity among groups, students, and exercises within the context of group learning activities, along with the noise present in their interactions, resulting in less robust and suboptimal diagnosis performance. To this end, in this paper, we propose DGCD, an adaptive Denoising graph neural network for realizing effective Group-level Cognitive Diagnosis. Specifically, we first construct a group-student-exercise (GSE) graph to explicitly model higher-order connectivity among groups, students, and exercises, contributing to the acquisition of informative representations. Then, we carefully design an adaptive denoising module, integrated into the graph neural network, to model the reliability distribution of student-exercise edges for mining purer interaction features. In particular, edges of lower reliability are more prone to exclusion, thereby reducing the impact of noisy interactions. Furthermore, recognizing the relational imbalance in the GSE graph, which could potentially introduce bias during message passing, we propose an entropy-weighted balance module to mitigate such bias. Finally, extensive experiments conducted on four real-world educational datasets clearly demonstrate the effectiveness of our proposed DGCD model. The code is available at https://github.com/BIMK/Intelligent-Education/tree/main/DGCD.
Keywords:
Data Mining: DM: Applications
Multidisciplinary Topics and Applications: MTA: Education