Integrate Question Information With Learning Behavior for Knowledge Tracing

• Published in: IEEE access Vol. 13; pp. 33532 - 33543
• Main Authors: Su, Sheng, Zeng, Pingfei, Kang, Chunhua, Xin, Tao
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Accuracy; Adaptation models; Analytical models; Bayes methods; behavioural collaboration; deep representation; Education; Knowledge; Knowledge acquisition; Knowledge tracing; Learning; learning behaviour; Monitoring; Predictive models; Problem-solving; Students; subject information; Tracing; Tracking; Trajectory; Tutorials
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2025.3543536

This paper explores Knowledge Tracing (KT) and its role in modeling students' knowledge progression over time. While traditional approaches such as Bayesian Knowledge Tracing and Deep Knowledge Tracing have established a solid foundation in this field, previous research often overlooked the complex interactions between problem features and learner behaviors. To address this gap, this paper introduces the Knowledge Tracking Model for Integrating Question Information and Learning Behaviour (IQILB-KT), a novel framework that enhances the precision of knowledge tracing in online educational systems. By integrating the effects of question characteristics and student behaviour via a channel attention mechanism, the model captures deeper insights into both question information and the learning process. Furthermore, the IQILB-KT model employs a Bottleneck Attention Module (BAM) to calculate learning behaviour synergies, replacing the previous reliance on covariance statistics. This enhances the stability and accuracy of the model when dealing with learning behaviour synergy data. Finally, the efficacy of the IQILB-KT model was validated through rigorous testing on two real-world datasets from the ASSISTments educational platform. The results demonstrate that the model outperforms all existing methods in predicting students' future performance and tracking their knowledge states in real time.