  Cognitive diagnosis seeks to estimate the cognitive states of students by
exploring their logged practice quiz data. It plays a pivotal role in
personalized learning guidance within intelligent education systems. In this
paper, we focus on an important, practical, yet often underexplored task:
domain-level zero-shot cognitive diagnosis (DZCD), which arises due to the
absence of student practice logs in newly launched domains. Recent cross-domain
diagnostic models have been demonstrated to be a promising strategy for DZCD.
These methods primarily focus on how to transfer student states across domains.
However, they might inadvertently incorporate non-transferable information into
student representations, thereby limiting the efficacy of knowledge transfer.
To tackle this, we propose Zero-1-to-3, a domain-level zero-shot cognitive
diagnosis framework via one batch of early-bird students towards three
diagnostic objectives. Our approach initiates with pre-training a diagnosis
model with dual regularizers, which decouples student states into domain-shared
and domain-specific parts. The shared cognitive signals can be transferred to
the target domain, enriching the cognitive priors for the new domain, which
ensures the cognitive state propagation objective. Subsequently, we devise a
strategy to generate simulated practice logs for cold-start students through
analyzing the behavioral patterns from early-bird students, fulfilling the
domain-adaption goal. Consequently, we refine the cognitive states of
cold-start students as diagnostic outcomes via virtual data, aligning with the
diagnosis-oriented goal. Finally, extensive experiments on six real-world
datasets highlight the efficacy of our model for DZCD and its practical
application in question recommendation. The code is publicly available at
https://github.com/bigdata-ustc/Zero-1-to-3.
