  Cognitive diagnosis is a crucial task in computational education, aimed at
evaluating students' proficiency levels across various knowledge concepts
through exercises. Current models, however, primarily rely on students'
answered exercises, neglecting the complex and rich information contained in
un-interacted exercises. While recent research has attempted to leverage the
data within un-interacted exercises linked to interacted knowledge concepts,
aiming to address the long-tail issue, these studies fail to fully explore the
informative, un-interacted exercises related to broader knowledge concepts.
This oversight results in diminished performance when these models are applied
to comprehensive datasets. In response to this gap, we present the
Collaborative-aware Mixed Exercise Sampling (CMES) framework, which can
effectively exploit the information present in un-interacted exercises linked
to un-interacted knowledge concepts. Specifically, we introduce a novel
universal sampling module where the training samples comprise not merely raw
data slices, but enhanced samples generated by combining weight-enhanced
attention mixture techniques. Given the necessity of real response labels in
cognitive diagnosis, we also propose a ranking-based pseudo feedback module to
regulate students' responses on generated exercises. The versatility of the
CMES framework bolsters existing models and improves their adaptability.
Finally, we demonstrate the effectiveness and interpretability of our framework
through comprehensive experiments on real-world datasets.
