Fusing Interaction Transition Features and Cross-Attention for Knowledge Tracing in E-Learning Systems

E-learning systems, with their flexibility across time and location, further highlight the importance of consumer electronics in education. Knowledge tracing (KT) is a key part, which depicts the state of students’ knowledge state and predicts students’ future performance. However, due to the sparsity of data in real scenarios, the adequacy of state modeling and the accuracy of prediction are affected. To address the above problem, we propose a method (FTCKT) that fuses interaction transition features and cross-attention relations to monitor the state of students’ knowledge by synergistically modeling global semantics and local semantics. Specifically, we employ a directed graph-based global information modeling module that aims to thoroughly extract exercise transition features, further refining the completeness of knowledge state modeling. Furthermore, we also designed a cross-domain attention fusion module to facilitate the integration of complementary information. The fusion module captures the long-term intent of the target exercise by modeling the dependencies of sequence domain, while synergistically modeling with the relational unit of graph domain to fully mine and integrate the long-term dependencies and interaction information among the interaction sequences. The experimental results show that dual-domain collaborative modeling can effectively extract interaction features, enhancing the model’s state modeling and prediction performance.