  Cognitive diagnosis aims to gauge students' mastery levels based on their
response logs. Serving as a pivotal module in web-based online intelligent
education systems (WOIESs), it plays an upstream and fundamental role in
downstream tasks like learning item recommendation and computerized adaptive
testing. WOIESs are open learning environment where numerous new students
constantly register and complete exercises. In WOIESs, efficient cognitive
diagnosis is crucial to fast feedback and accelerating student learning.
However, the existing cognitive diagnosis methods always employ intrinsically
transductive student-specific embeddings, which become slow and costly due to
retraining when dealing with new students who are unseen during training. To
this end, this paper proposes an inductive cognitive diagnosis model (ICDM) for
fast new students' mastery levels inference in WOIESs. Specifically, in ICDM,
we propose a novel student-centered graph (SCG). Rather than inferring mastery
levels through updating student-specific embedding, we derive the inductive
mastery levels as the aggregated outcomes of students' neighbors in SCG.
Namely, SCG enables to shift the task from finding the most suitable
student-specific embedding that fits the response logs to finding the most
suitable representations for different node types in SCG, and the latter is
more efficient since it no longer requires retraining. To obtain this
representation, ICDM consists of a
construction-aggregation-generation-transformation process to learn the final
representation of students, exercises and concepts. Extensive experiments
across real-world datasets show that, compared with the existing cognitive
diagnosis methods that are always transductive, ICDM is much more faster while
maintains the competitive inference performance for new students.
