Construction of a collaborative learning grouping model: Integration of knowledge complementarity and dynamic diagnosis.
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| Title: | Construction of a collaborative learning grouping model: Integration of knowledge complementarity and dynamic diagnosis. |
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| Authors: | Li, Haojun1 424389370@qq.com, Chen, Yaohan1 1635058484@qq.com, Liao, Weixia2 359579851@qq.com, Wang, Xuhui3 zgdlhj@zjut.edu.cn |
| Source: | Educational Technology & Society. Jul2026, Vol. 29 Issue 3, p19-33. 15p. |
| Subject Terms: | *Collaborative learning, *Dynamic assessment (Education), *Machine learning, K-means clustering |
| Abstract: | In the process of collaborative learning, effective grouping is the key to improving learning efficiency. A well-structured collaborative learning group can markedly improve the learning efficiency of both individuals and the group as a whole. Nonetheless, current grouping methods for collaborative learning frequently fall short of a comprehensive assessment of students' knowledge proficiency, thereby failing to guarantee that group members' knowledge structures are mutually complementary. Therefore, a grouping model combining knowledge complementarity and dynamic diagnosis is proposed. The grouping model is divided into five layers. Firstly, the learning objective analysis layer deconstructs the teaching content into several core knowledge points. On this basis, the dynamic knowledge diagnosis layer uses the improved deep knowledge tracking model DKVMN-KT to dynamically diagnose students' mastery of core knowledge points. Then, in the personalized grouping layer, K-means algorithm is used to cluster the personalized grouping results. Furthermore, teachers were involved in the manual fine-tuning of the grouping results. Finally, the model forms a closed-loop feedback link through a deep interactive optimization layer to promote the iterative optimization of the collaborative learning grouping model. The experimental results indicate that the collaborative learning grouping model is helpful to effectively group students at the level of knowledge structure, and can improve the efficiency of collaborative learning, In order to guarantee more equitable and diverse grouping outcomes, this methodology promotes positive interplay among students, enabling them to mutually learn from one another and thereby enhance their comprehension of diverse knowledge points effectively. [ABSTRACT FROM AUTHOR] |
| Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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: | Education Research Complete |
| FullText | Links: – Type: pdflink Text: Availability: 0 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 195015194 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Construction of a collaborative learning grouping model: Integration of knowledge complementarity and dynamic diagnosis. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Li%2C+Haojun%22">Li, Haojun</searchLink><relatesTo>1</relatesTo><i> 424389370@qq.com</i><br /><searchLink fieldCode="AR" term="%22Chen%2C+Yaohan%22">Chen, Yaohan</searchLink><relatesTo>1</relatesTo><i> 1635058484@qq.com</i><br /><searchLink fieldCode="AR" term="%22Liao%2C+Weixia%22">Liao, Weixia</searchLink><relatesTo>2</relatesTo><i> 359579851@qq.com</i><br /><searchLink fieldCode="AR" term="%22Wang%2C+Xuhui%22">Wang, Xuhui</searchLink><relatesTo>3</relatesTo><i> zgdlhj@zjut.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Educational+Technology+%26+Society%22">Educational Technology & Society</searchLink>. Jul2026, Vol. 29 Issue 3, p19-33. 15p. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Dynamic+assessment+%28Education%29%22">Dynamic assessment (Education)</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22K-means+clustering%22">K-means clustering</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: In the process of collaborative learning, effective grouping is the key to improving learning efficiency. A well-structured collaborative learning group can markedly improve the learning efficiency of both individuals and the group as a whole. Nonetheless, current grouping methods for collaborative learning frequently fall short of a comprehensive assessment of students' knowledge proficiency, thereby failing to guarantee that group members' knowledge structures are mutually complementary. Therefore, a grouping model combining knowledge complementarity and dynamic diagnosis is proposed. The grouping model is divided into five layers. Firstly, the learning objective analysis layer deconstructs the teaching content into several core knowledge points. On this basis, the dynamic knowledge diagnosis layer uses the improved deep knowledge tracking model DKVMN-KT to dynamically diagnose students' mastery of core knowledge points. Then, in the personalized grouping layer, K-means algorithm is used to cluster the personalized grouping results. Furthermore, teachers were involved in the manual fine-tuning of the grouping results. Finally, the model forms a closed-loop feedback link through a deep interactive optimization layer to promote the iterative optimization of the collaborative learning grouping model. The experimental results indicate that the collaborative learning grouping model is helpful to effectively group students at the level of knowledge structure, and can improve the efficiency of collaborative learning, In order to guarantee more equitable and diverse grouping outcomes, this methodology promotes positive interplay among students, enabling them to mutually learn from one another and thereby enhance their comprehension of diverse knowledge points effectively. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Educational Technology & Society is the property of International Forum of Educational Technology & Society (IFETS) 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.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=ehh&AN=195015194 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.30191/ETS.202607_29(3).RP02 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 15 StartPage: 19 Subjects: – SubjectFull: Collaborative learning Type: general – SubjectFull: Dynamic assessment (Education) Type: general – SubjectFull: Machine learning Type: general – SubjectFull: K-means clustering Type: general Titles: – TitleFull: Construction of a collaborative learning grouping model: Integration of knowledge complementarity and dynamic diagnosis. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Li, Haojun – PersonEntity: Name: NameFull: Chen, Yaohan – PersonEntity: Name: NameFull: Liao, Weixia – PersonEntity: Name: NameFull: Wang, Xuhui IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 07 Text: Jul2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 11763647 Numbering: – Type: volume Value: 29 – Type: issue Value: 3 Titles: – TitleFull: Educational Technology & Society Type: main |
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