Improving Cognitive Diagnosis Models with Adaptive Relational Graph
  Neural Networks
Pengyang Shao Chen Gao Lei Chen Yonghui Yang Kun Zhang Meng Wang
Cognitive Diagnosis (CD) algorithms receive growing research interest in
intelligent education. Typically, these CD algorithms assist students by
inferring their abilities (i.e., their proficiency levels on various knowledge
concepts). The proficiency levels can enable further targeted skill training
and personalized exercise recommendations, thereby promoting students' learning
efficiency in online education. Recently, researchers have found that building
and incorporating a student-exercise bipartite graph is beneficial for
enhancing diagnostic performance. However, there are still limitations in their
studies. On one hand, researchers overlook the heterogeneity within edges,
where there can be both correct and incorrect answers. On the other hand, they
disregard the uncertainty within edges, e.g., a correct answer can indicate
true mastery or fortunate guessing. To address the limitations, we propose
Adaptive Semantic-aware Graph-based Cognitive Diagnosis model (ASG-CD), which
introduces a novel and effective way to leverage bipartite graph information in
CD. Specifically, we first map students, exercises, and knowledge concepts into
a latent representation space and combine these latent representations to
obtain student abilities and exercise difficulties. After that, we propose a
Semantic-aware Graph Neural Network Layer to address edge heterogeneity. This
layer splits the original bipartite graph into two subgraphs according to edge
semantics, and aggregates information based on these two subgraphs separately.
To mitigate the impact of edge uncertainties, we propose an Adaptive Edge
Differentiation Layer that dynamically differentiates edges, followed by
keeping reliable edges and filtering out uncertain edges. Extensive experiments
on three real-world datasets have demonstrated the effectiveness of ASG-CD.