[论文解读] DisenGCD: A Meta Multigraph-assisted Disentangled Graph Learning Framework for Cognitive Diagnosis

Shangshang Yang, Mingyang Chen+5 more|arXiv (Cornell University)|Oct 23, 2024

Machine Learning in Healthcare被引用 4

一句话总结

该论文提出DisenGCD，一种元多图辅助的解耦图学习框架，用于认知诊断，通过在三种不同图结构（交互图、关系图、依赖图）上解耦学生、练习题和概念的表征，实现更优的性能和对交互噪声的更强鲁棒性。通过使用可学习的元多图模块，利用低阶练习题特征增强学生表征，并结合图注意力网络（GATs）进行练习题/概念学习，DisenGCD在性能和抗噪性方面均优于当前最先进方法。

ABSTRACT

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 extcolor{red}{\url{https://github.com/BIMK/Intelligent-Education/tree/main/DisenGCD}}.

研究动机与目标

• 解决现有基于图学习的认知诊断模型在学生交互数据噪声下的鲁棒性差的问题。
• 克服统一图模型无法保留练习题和概念的交互无关表征的局限性。
• 通过利用浅层GNN层中的低阶练习题潜在特征，改进学生表征。
• 设计一种解耦学习框架，分别在不同图结构上分离学生、练习题和概念的表征学习。
• 设计一种新型诊断函数，整合三种解耦表征以实现准确的响应预测。

提出的方法

• 构建三种解耦图：(1) 学生-练习题-概念交互图，(2) 练习题-概念关系图，以及(3) 概念依赖图。
• 分别在关系图和依赖图上使用图注意力网络（GATs）学习练习题和概念的表征。
• 在交互图上实现一个元多图学习模块，以支持学生表征的多条可学习传播路径。
• 利用元多图模块，使当前的学生表征能够访问并聚合来自浅层的低阶练习题表征。
• 通过一种新型诊断函数整合三种解耦表征，实现最终的响应预测。
• 使用学生响应预测的交叉熵损失，端到端训练模型。

实验结果

研究问题

• RQ1 解耦图学习框架是否能提升认知诊断中对噪声学生交互数据的鲁棒性？
• RQ2 所提出的元多图模块是否能通过访问低阶练习题特征有效增强学生表征？
• RQ3 所学习的元多图结构在具有不同练习题-概念结构的多种数据集上是否具备泛化能力？
• RQ4 与统一图模型相比，该解耦框架在性能和鲁棒性方面表现如何？
• RQ5 各组件（解耦图、元多图模块、诊断函数）对整体模型性能的贡献分别是什么？

主要发现

• DisenGCD在三个基准数据集上优于当前最先进认知诊断模型，AUC和准确率均更高。
• 消融实验证实，在三种解耦图上学习表征相比统一图学习显著提升了性能。
• 采用可学习路径的元多图模块在性能上优于朴素嵌入和预定义路径结构。
• 所学习的元多图结构在相似数据集（如SLP和Math）上表现有效，但在差异较大的数据集（如ASSISTments）上效果较弱，表明其具有数据集特定的适应性。
• DisenGCD在交互噪声下表现出高度鲁棒性，数据扰动下性能保持稳定。
• 可视化结果表明，元多图在不同数据集上学习到不同的结构，反映出练习题-概念关系的内在差异。