A Dual-Graph Convolutional Knowledge Tracking Method Based On Hidden Knowledge Extraction.
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| Title: | A Dual-Graph Convolutional Knowledge Tracking Method Based On Hidden Knowledge Extraction. |
|---|---|
| Authors: | Feng, Wenfang1 1036784024@qq.com, Cao, Hang2 3118700173@qq.com, Zhao, Minrui3 2904104078@qq.com, Xia, Zhiyuan3 xia15094905773@163.com |
| Source: | IAENG International Journal of Computer Science. Jul2026, Vol. 53 Issue 7, p2852-2865. 14p. |
| Subjects: | Hypergraphs, Graph neural networks, Learning analytics, Deep learning, Machine learning |
| Abstract: | Knowledge tracking (KT) is a methodology that leverages students' past responses to assess their current mastery of knowledge and predict their future proficiency in related concepts and exercises. However, existing KT approaches face several challenges, including inadequate representation of knowledge concepts and exercises, as well as insufficient consideration of the multiple relationships between them. To address these issues, this paper proposes a novel Dual-Graph Convolutional Knowledge Tracking method based on Hidden Knowledge Extraction (DGHKE). Specifically, multivariate relational information is modeled using concept-exercise association graphs and exercise-exercise association graphs, constructed via hypergraph and directed graph representations to facilitate effective representation learning. Furthermore, the HKIE-LSTM attention mechanism is employed to capture deep knowledge information potentially overlooked by graph convolution, integrating it with original features to enhance the representation of both concepts and exercises and mitigate the sparsity problem commonly observed in KT data. Online supervised models then exploit the fused information to enable mutual learning of representations, while additional supervised signals provide feedback to further improve prediction accuracy. Extensive experiments on three benchmark datasets, compared against seven state-of-the-art baselines, demonstrate that DGHKE consistently achieves superior performance. [ABSTRACT FROM AUTHOR] |
| Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 195088911 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A Dual-Graph Convolutional Knowledge Tracking Method Based On Hidden Knowledge Extraction. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Feng%2C+Wenfang%22">Feng, Wenfang</searchLink><relatesTo>1</relatesTo><i> 1036784024@qq.com</i><br /><searchLink fieldCode="AR" term="%22Cao%2C+Hang%22">Cao, Hang</searchLink><relatesTo>2</relatesTo><i> 3118700173@qq.com</i><br /><searchLink fieldCode="AR" term="%22Zhao%2C+Minrui%22">Zhao, Minrui</searchLink><relatesTo>3</relatesTo><i> 2904104078@qq.com</i><br /><searchLink fieldCode="AR" term="%22Xia%2C+Zhiyuan%22">Xia, Zhiyuan</searchLink><relatesTo>3</relatesTo><i> xia15094905773@163.com</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22IAENG+International+Journal+of+Computer+Science%22">IAENG International Journal of Computer Science</searchLink>. Jul2026, Vol. 53 Issue 7, p2852-2865. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Hypergraphs%22">Hypergraphs</searchLink><br /><searchLink fieldCode="DE" term="%22Graph+neural+networks%22">Graph neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Knowledge tracking (KT) is a methodology that leverages students' past responses to assess their current mastery of knowledge and predict their future proficiency in related concepts and exercises. However, existing KT approaches face several challenges, including inadequate representation of knowledge concepts and exercises, as well as insufficient consideration of the multiple relationships between them. To address these issues, this paper proposes a novel Dual-Graph Convolutional Knowledge Tracking method based on Hidden Knowledge Extraction (DGHKE). Specifically, multivariate relational information is modeled using concept-exercise association graphs and exercise-exercise association graphs, constructed via hypergraph and directed graph representations to facilitate effective representation learning. Furthermore, the HKIE-LSTM attention mechanism is employed to capture deep knowledge information potentially overlooked by graph convolution, integrating it with original features to enhance the representation of both concepts and exercises and mitigate the sparsity problem commonly observed in KT data. Online supervised models then exploit the fused information to enable mutual learning of representations, while additional supervised signals provide feedback to further improve prediction accuracy. Extensive experiments on three benchmark datasets, compared against seven state-of-the-art baselines, demonstrate that DGHKE consistently achieves superior performance. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of IAENG International Journal of Computer Science is the property of International Association of Engineers (IAENG) and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 2852 Subjects: – SubjectFull: Hypergraphs Type: general – SubjectFull: Graph neural networks Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Machine learning Type: general Titles: – TitleFull: A Dual-Graph Convolutional Knowledge Tracking Method Based On Hidden Knowledge Extraction. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Feng, Wenfang – PersonEntity: Name: NameFull: Cao, Hang – PersonEntity: Name: NameFull: Zhao, Minrui – PersonEntity: Name: NameFull: Xia, Zhiyuan IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 1819656X Numbering: – Type: volume Value: 53 – Type: issue Value: 7 Titles: – TitleFull: IAENG International Journal of Computer Science Type: main |
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