  Cognitive diagnosis plays a vital role in modern intelligent education
platforms to reveal students' proficiency in knowledge concepts for subsequent
adaptive tasks. However, due to the requirement of high model interpretability,
existing manually designed cognitive diagnosis models hold too simple
architectures to meet the demand of current intelligent education systems,
where the bias of human design also limits the emergence of effective cognitive
diagnosis models. In this paper, we propose to automatically design novel
cognitive diagnosis models by evolutionary multi-objective neural architecture
search (NAS). Specifically, we observe existing models can be represented by a
general model handling three given types of inputs and thus first design an
expressive search space for the NAS task in cognitive diagnosis. Then, we
propose multi-objective genetic programming (MOGP) to explore the NAS task's
search space by maximizing model performance and interpretability. In the MOGP
design, each architecture is transformed into a tree architecture and encoded
by a tree for easy optimization, and a tailored genetic operation based on four
sub-genetic operations is devised to generate offspring effectively. Besides,
an initialization strategy is also suggested to accelerate the convergence by
evolving half of the population from existing models' variants. Experiments on
two real-world datasets demonstrate that the cognitive diagnosis models
searched by the proposed approach exhibit significantly better performance than
existing models and also hold as good interpretability as human-designed
models.
