Revisiting Cognition in Neural Cognitive Diagnosis

Cognitive diagnosis is a fundamental task in intelligent education, aiming to measure students' proficiency on knowledge concepts based on practice data. Traditional methods utilize a broadly-defined latent trait θ to represent knowledge proficiency with some cognitive factors like skill or ability. However, existing methods simplify this to a narrowly-defined latent trait θ, which focuses only on knowledge or treats these cognitive factors as implicit features inferred from data. They fail to explicitly model these cognitive factors, resulting in limited performance and interpretability. To this end, we revisit essence of cognition in Educational Psychology Theory and propose a novel Cognition-aware Cognitive Diagnosis (CCD) model, where we first introduce the Cognition factor as a bridge into the long-standing three-basic-factors (Student, Exercise, Knowledge concept) paradigm. CCD has two main parts: cognition representations and a two-stage diagnostic process. In the first part, we explicitly model cognitive process (CP) dimensions from Bloom's Taxonomy of Educational Objectives, leading to two innovative concepts proposed: the student's Subjective Cognitive Ability (SCA) and the exercise's Objective Cognitive Attribute (OCA), derived by regulating the CP through S-K and E-K interactions, respectively. Then, the SCA and OCA are formed into a new cognition-aware latent trait θ. In the second part, we employ a basic interaction function and a slip and guess influence function, inputting our new θ, a continuous Q-matrix (generated by a siamese PLMs), and other features to obtain the ideal result, followed by feeding it into the slip and guess influence function to obtain the actual result. Extensive experiments on real-world datasets demonstrates the superior effectiveness and good interpretability.