Exploring long- and short-term knowledge state graph representations with adaptive fusion for knowledge tracing

Knowledge Tracing (KT) is an important research area in online education that focuses on predicting future academic performance based on students’ historical exercise records. The key to solving the KT problem lies in assessing students’ knowledge states through their responses to concept-related exercises. However, analyzing exercise records from a single perspective does not provide a comprehensive model of student knowledge. The truth is that students’ knowledge states often exhibit long- and short-term phenomena, corresponding to long-term knowledge systems and short-term real-time learning, both of which are closely related to learning quality and preferences. Existing studies have often neglected the learning preferences implied by long-term knowledge states and their impact on student performance. Therefore, we introduce a hybrid knowledge tracing model that utilizes both long- and short-term knowledge state representations (L-SKSKT). It enhances KT by fusing these two types of knowledge state representations and measuring their impact on learning quality. L-SKSKT includes a graph construction method designed to model students’ long- and short-term knowledge states. In addition, L-SKSKT incorporates a knowledge state graph embedding model that can effectively capture long- and short-term dependencies, generating corresponding knowledge state representations. Furthermore, we propose a fusion mechanism to integrate these representations and trace their impact on learning outcomes. Extensive empirical results on four benchmark datasets show that our approach achieves the best performance for KT, and beats various strong baselines with a large margin. • We design a method to transform exercise records into long- and short-term knowledge graphs. • We propose a hierarchical knowledge state graph embedding model with adaptive fusion. • Extensive experiments on four KT datasets show our method outperforms strong baselines.