DHKFN: Knowledge Tracking Model Based on Deep Hierarchical Knowledge Fusion Networks

Pei Zhang, Ping Hu, Gang Li, Yingying Lv and Meng Wan
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Pei Zhang: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
Ping Hu: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
Gang Li: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
Yingying Lv: School of Information Engineering, Henan Institute of Science and Technology, Xinxiang, China
Meng Wan: School of Information and Media, Sanmenxia College of Applied Engineering, Sanmenxia, China

International Journal of Knowledge Management (IJKM), 2024, vol. 20, issue 1, 1-16

Abstract: Knowledge tracing is effective in modeling learners' knowledge levels to predict future answering situations based on their past learning history and interaction processes. However, current methods often overlook the impact of knowledge point correlations on answer prediction. This paper design a model based on deep hierarchical knowledge fusion networks (DHKFN). It utilizes statistical approaches to construct correlation matrices to capture the correlations between knowledge points. Subsequently, it constructs a topology graph structure of questions and knowledge points and utilizes a multi-head attention network to learn student interaction information from multiple perspectives, effectively constructing adjacency matrices. Finally, it uses GCN to learn the interaction information representation between deep-level knowledge points from dynamically constructed a topology graph structures. Experimental results on three large public datasets show that DHKFN effectively considers the influence of correlations between different knowledge points, thus showing promising effectiveness.

Date: 2024
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