GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing
文献类型：期刊论文
| 作者 | Xinhua Wang; Shasha Zhao; Lei Guo; Lei Zhu; Chaoran Cui; Liancheng Xu |
| 刊名 | IEEE/CAA Journal of Automatica Sinica
 |
| 出版日期 | 2023 |
| 卷号 | 10期号:11页码:2108-2123 |
| ISSN号 | 2329-9266 |
| 关键词 | Contrastive learning counterfactual representation graph neural network knowledge tracing |
| DOI | 10.1109/JAS.2023.123678 |
| 英文摘要 | With the popularity of online learning in educational settings, knowledge tracing (KT) plays an increasingly significant role. The task of KT is to help students learn more effectively by predicting their next mastery of knowledge based on their historical exercise sequences. Nowadays, many related works have emerged in this field, such as Bayesian knowledge tracing and deep knowledge tracing methods. Despite the progress that has been made in KT, existing techniques still have the following limitations: 1) Previous studies address KT by only exploring the observational sparsity data distribution, and the counterfactual data distribution has been largely ignored. 2) Current works designed for KT only consider either the entity relationships between questions and concepts, or the relations between two concepts, and none of them investigates the relations among students, questions, and concepts, simultaneously, leading to inaccurate student modeling. To address the above limitations, we propose a graph counterfactual augmentation method for knowledge tracing. Concretely, to consider the multiple relationships among different entities, we first uniform students, questions, and concepts in graphs, and then leverage a heterogeneous graph convolutional network to conduct representation learning. To model the counterfactual world, we conduct counterfactual transformations on students’ learning graphs by changing the corresponding treatments and then exploit the counterfactual outcomes in a contrastive learning framework. We conduct extensive experiments on three real-world datasets, and the experimental results demonstrate the superiority of our proposed GraphCA method compared with several state-of-the-art baselines. |
| 源URL | [http://ir.ia.ac.cn/handle/173211/52427]  |
| 专题 | 自动化研究所_学术期刊_IEEE/CAA Journal of Automatica Sinica |
| 推荐引用方式 GB/T 7714 | Xinhua Wang,Shasha Zhao,Lei Guo,et al. GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing[J]. IEEE/CAA Journal of Automatica Sinica,2023,10(11):2108-2123. |
| APA | Xinhua Wang,Shasha Zhao,Lei Guo,Lei Zhu,Chaoran Cui,&Liancheng Xu.(2023).GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing.IEEE/CAA Journal of Automatica Sinica,10(11),2108-2123. |
| MLA | Xinhua Wang,et al."GraphCA: Learning From Graph Counterfactual Augmentation for Knowledge Tracing".IEEE/CAA Journal of Automatica Sinica 10.11(2023):2108-2123. |
入库方式： OAI收割
来源：自动化研究所
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