  Cognitive diagnosis is a fundamental and critical task in learning
assessment, which aims to infer students' proficiency on knowledge concepts
from their response logs. Current works assume each knowledge concept will
certainly be tested and covered by multiple exercises. However, whether online
or offline courses, it's hardly feasible to completely cover all knowledge
concepts in several exercises. Restricted tests lead to undiscovered knowledge
deficits, especially untested knowledge concepts(UKCs). In this paper, we
propose a novel \underline{Dis}entangling Heterogeneous \underline{K}nowledge
\underline{C}ognitive \underline{D}iagnosis framework on untested
knowledge(DisKCD). Specifically, we leverage course grades, exercise questions,
and resources to learn the potential representations of students, exercises,
and knowledge concepts. In particular, knowledge concepts are disentangled into
tested and untested based on the limiting actual exercises. We construct a
heterogeneous relation graph network via students, exercises, tested knowledge
concepts(TKCs), and UKCs. Then, through a hierarchical heterogeneous
message-passing mechanism, the fine-grained relations are incorporated into the
embeddings of the entities. Finally, the embeddings will be applied to multiple
existing cognitive diagnosis models to infer students' proficiency on UKCs.
Experimental results on real-world datasets show that the proposed model can
effectively improve the performance of the task of diagnosing students'
proficiency on UKCs. Our anonymous code is available at
https://anonymous.4open.science/r/DisKCD.
