Enhancing Knowledge Tracing with Multi-hierarchy Hypergraph Adaptive Knowledge Transfer

What the paper says

Knowledge tracing aims to model learners’ cognitive state dynamically from interaction sequences to support personalized instructional decisions. While existing methods achieve good prediction accuracy, they often overlook the transfer effects between knowledge concepts (KCs) and their propagation, limiting fine-grained, structured modeling of mastery and overall performance. Although some studies incorporate knowledge transfer using predefined KC similarity graphs, they assume static transfer structures for all learners, neglecting the continuous evolution of transfer abilities due to interventions and self-regulation, and focus solely on KC-hierarchy relations. To overcome these limitations, we propose MHAKT. MHAKT operates across multiple hierarchies of knowledge components through three modules: 1) Transfer perception module utilizes a masked attention mechanism to identify the contribution of the Top- \(K\) most relevant historical interactions for the target knowledge component, dynamically updating the learner-specific transfer structure; 2) Knowledge transfer module employs hypergraph neural networks to comprehensively model many-to-many transfer processes among knowledge components; 3) Cognition update module consolidates new knowledge while applying forgetting mechanisms to update the learner's cognitive state. Extensive experiments on benchmark datasets demonstrate that MHAKT significantly outperforms thirteen baseline models. In particular, under data sparsity and generalization settings designed to simulate cold-start knowledge components, MHAKT shows strong robustness and maintains superior predictive accuracy. Ablation studies and exploratory experiments further validate the essential contribution of each module, and visualization analyses further reveal MHAKT's potential for explainable modeling.