  Cognitive diagnosis is a fundamental yet critical research task in the field
of intelligent education, which aims to discover the proficiency level of
different students on specific knowledge concepts. Despite the effectiveness of
existing efforts, previous methods always considered the mastery level on the
whole students, so they still suffer from the Long Tail Effect. A large number
of students who have sparse data are performed poorly in the model. To relieve
the situation, we proposed a Self-supervised Cognitive Diagnosis (SCD)
framework which leverages the self-supervised manner to assist the graph-based
cognitive diagnosis, then the performance on those students with sparse data
can be improved. Specifically, we came up with a graph confusion method that
drops edges under some special rules to generate different sparse views of the
graph. By maximizing the consistency of the representation on the same node
under different views, the model could be more focused on long-tailed students.
Additionally, we proposed an importance-based view generation rule to improve
the influence of long-tailed students. Extensive experiments on real-world
datasets show the effectiveness of our approach, especially on the students
with sparse data.
