TOWARDS A UNIFIED FRAMEWORK FOR KNOWLEDGE TRACING WITH GRAPH CONVOLUTIONAL AND NEURAL ARCHITECTURES
Yaxi Su1, Mohamad Yusof Darus2*, Norizan Mat Diah3, Jinpeng Huang4, and
Azlin Ramli5
1,2*,3,5Faculty of Computer and Mathematical Sciences.Universiti Teknologi MARA (UiTM),
40450, Shah Alam, Selangor
4Cloud Computing Technology and Application Department, Shandong Institute of Commerce
and Technology, Shandong, China, 250103
1This email address is being protected from spambots. You need JavaScript enabled to view it., 2*This email address is being protected from spambots. You need JavaScript enabled to view it., 3This email address is being protected from spambots. You need JavaScript enabled to view it.,
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ABSTRACT
This paper sets out to propose a unified theoretical framework for knowledge tracing (KT) that combines graph convolutional networks (GCNs) with neural sequence architectures in intelligent tutoring systems. While existing methods have achieved some success, they face limitations in modelling relational dependencies among concepts and the temporal progression of learner behaviour. Building on socio-constructivist views of knowledge as a network of relations and connectionist accounts of learning as adaptation over time, the framework integrates graph-based relational reasoning with sequence-based temporal modelling. The argument advanced here is that the integration offers interpretable representations of knowledge states while preserving predictive performance. The paper draws together recent developments in graph-enhanced KT and attention-based models and outlines design heuristics for scalable deployment. Key issues are identified, including computational cost, data sparsity, and explainability for classroom use. It is anticipated that the framework will inform the design of more systems and provide a tractable agenda for empirical validation across multiple domains and learner populations.
Keywords: Adaptive Learning, Graph Convolutional Networks, Intelligent Tutoring Systems, Knowledge Tracing, Neural Sequence Models, Socio-Constructivist models
Published On: 1 October 2025
