Enhancing Cognitive Diagnosis by Modeling Learner Cognitive Structure State

TL;DR

This paper introduces a novel framework, CSCD, that models both knowledge mastery and the relationships between concepts using graph attention networks to improve cognitive diagnosis accuracy and interpretability.

Contribution

It presents an innovative framework that effectively models learner's cognitive structures, including knowledge states and knowledge structure states, using graph attention networks.

Findings

Superior diagnostic accuracy on real datasets

Enhanced interpretability of cognitive diagnosis

Effective integration of knowledge and knowledge structure states

Abstract

Cognitive diagnosis represents a fundamental research area within intelligent education, with the objective of measuring the cognitive status of individuals. Theoretically, an individual's cognitive state is essentially equivalent to their cognitive structure state. Cognitive structure state comprises two key components: knowledge state (KS) and knowledge structure state (KUS). The knowledge state reflects the learner's mastery of individual concepts, a widely studied focus within cognitive diagnosis. In contrast, the knowledge structure state-representing the learner's understanding of the relationships between concepts-remains inadequately modeled. A learner's cognitive structure is essential for promoting meaningful learning and shaping academic performance. Although various methods have been proposed, most focus on assessing KS and fail to assess KUS. To bridge this gap, we propose an innovative and effective framework-CSCD (Cognitive Structure State-based Cognitive Diagnosis)-which introduces a novel framework to modeling learners' cognitive structures in diagnostic assessments, thereby offering new insights into cognitive structure modeling. Specifically, we employ an edge-feature-based graph attention network to represent the learner's cognitive structure state, effectively integrating KS and KUS. Extensive experiments conducted on real datasets demonstrate the superior performance of this framework in terms of diagnostic accuracy and interpretability.