Authors: Zhifu Chen, Hengnian Gu, Jin Peng Zhou, Dongdai Zhou
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.
Source: http://arxiv.org/abs/2412.19759v1
