A GCN and Autoencoder-Based Framework for Fine-Grained Diagnosis of Learners’ Cognitive Structures

Accurately diagnosing fine-grained differences in learners’ cognitive structures, which arise from their understanding of interdependencies among knowledge concepts, remains a significant challenge in intelligent education. To address this gap, this paper introduces a novel cognitive diagnosis framework that integrates Graph Convolutional Networks (GCNs) with autoencoders. The framework explicitly models the educational prerequisite relationships within a knowledge concept map to infer the structural features of a learner’s cognition. The concept map is first embedded into a structural matrix using struc2vec. A two-layer GCN then refines this matrix by integrating learner-specific mastery evidence derived from individual performance data using the Attribute Hierarchy Method (AHM), to generate a cognitive structure representation. Subsequently, an autoencoder compresses this representation into a low-dimensional vector, enabling the derivation of a cognitive structure coefficient. Empirical analyses of two real-world course datasets (XML: N = 65; Java: N = 38) showed strong associations between the proposed coefficient and course assessment scores (XML: Pearson’s r = 0.803, p < 0.001; Java: r = 0.776, p < 0.001). Case analyses further showed that learners with identical or similar aggregate scores could exhibit distinct concept-level profiles, providing the granularity needed for personalized learning interventions.