HKT: Hierarchical structure-based knowledge tracing

Knowledge tracing (KT) is a fundamental task in Intelligent Tutoring Systems, aiming to predict learners’ performance on specific questions and trace their evolving knowledge state. With the advancement of deep learning in this field, various methods have been applied to model the relations between knowledge. However, most existing knowledge tracing methods focus on modeling knowledge at a single level, neglecting the inherent hierarchical structure of knowledge, which limits their ability to capture complex relations. In this paper, we propose a novel hierarchical knowledge tracing model (HKT), which integrates influences of multiple knowledge levels to predict learners’ performance. Specifically, we construct different types of hierarchical graphs to capture both intra-hierarchy dependencies and cross-hierarchy relations. To effectively combine information from multiple levels, we design weight allocation networks that dynamically assign weights to different knowledge levels, thereby synthesizing their effects for accurate performance prediction. Experimental results demonstrate that HKT outperforms baseline methods on multiple benchmark datasets, validating the effectiveness of integrating knowledge across all levels compared to single-level models.