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Multi-Granularity Ensemble Interaction Graph Modeling for Knowledge Tracing
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AI
DOI：10.1016/j.knosys.2024.112834.png)
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Knowledge tracing (KT) is a crucial educational tool that models students' mastery of various knowledge concepts by analyzing their historical learning records. Mainstream studies have turned to Deep Neural Networks (DNNs) to effectively trace students' knowledge states. Nonetheless, these methods may encounter performance barriers due to two critical constraints: the treatment of learning records as singular time series data and the absence of accounting for interaction correlations. To overcome the limitations, we introduce a novel KT model, named M ulti-Granularity Ensemble I nteraction G raph Modeling for K nowledge T racing (MGIGKT). Our model incorporates two modules designed to model students' knowledge states from the perspectives of temporal perception and interaction dependency, respectively. This approach allows for a more comprehensive understanding of students' learning dynamics and the correlations between their interactions. We have conducted extensive experiments on four datasets to evaluate the superiority and effectiveness of our proposed MGIGKT model. The results of these experiments provide empirical evidence of the model's enhanced ability to trace students' knowledge states accurately.
Keyword:
Intelligent education
Knowledge tracing
Correctness prediction
Graph Neural Networks
期刊
K
IF:
7.6
论文数:
1.2W
被引数:
4.5W
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