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Interpretable knowledge tracing with dual-level knowledge states

delete2025-09-09
delete0
PRE
AI
李
李艳婷 (Yanting LI)
T
Tao Zhou
T
Tianyu Cai
S
Shenggen Ju *
DOI：10.1016/j.eswa.2025.129658delete
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Abstract

Abstract

En 中文
• Designs a RoLinear Transformer and review mechanism for problem-level knowledge state modeling. • Proposes three learning phases and employs GNN to capture influence propagation among concepts. • Integrates guess and slip parameters in IRT to enhance model interpretability. • Outperforms 20 KT models on five datasets with improved interpretability.

Journal

Expert Systems with Applications cover
Expert Systems with Applications
IF:
7.5
Papers:
2.9W
Citations:
10.2W

Organization

No organization information available