Academic Journal
Exploring the internal dynamics of collaborative engagement in high- and low-performing collaborative problem-solving groups.
| Τίτλος: | Exploring the internal dynamics of collaborative engagement in high- and low-performing collaborative problem-solving groups. |
|---|---|
| Συγγραφείς: | Hu, Wanqing, Li, Yanyan, Huang, Ruiyan, Gong, Rushi, Li, Xin |
| Πηγή: | Journal of Computing in Higher Education; Jun2026, Vol. 38 Issue 2, p704-731, 28p |
| Θεματικοί όροι: | Group problem solving, Cooperation, Learning analytics, Hidden Markov models, Social interaction, Deep learning, Attention, Participation |
| Περίληψη: | Revealing the dynamics of collaborative engagement and its association with collaborative problem-solving (CPS) outcomes is beneficial for guiding teachers to provide adaptive support during CPS processes. Although existing research has indicated that the various dimensions of collaborative engagement influence each other over time, it remains unclear how the internal dynamics of collaborative engagement are associated with CPS outcomes. Additionally, previous studies have primarily employed manual coding to analyze collaborative engagement, which limits the exploration of the dynamic characteristics of collaborative engagement in large-scale datasets. This study aimed to investigate the internal dynamics of collaborative engagement, encompassing behavioral, cognitive, socio-emotional engagement, and their relationship with CPS outcomes. Specifically, a learning analytics method based on deep learning models was employed for the automatic detection of collaborative engagement. Subsequently, according to the results of automated detection, hidden Markov modeling was utilized to investigate the difference in the internal dynamics of collaborative engagement between high- and low-performing groups. These investigations were grounded in a dataset comprising 57,400 utterances collected from 20 groups participating in a CPS activity within a university setting. The findings showed that compared with high-performing groups, low-performing groups were more likely to become stuck in the states of "Limited Cognitive Engagement" and "Lone Neutral Participation" during CPS process. Furthermore, high-performing groups were found to transition away from the collaborative engagement state of "Cognitive Conflict with Confusion" by enhancing behavioral engagement and deepening cognitive engagement. While the low-performing groups remained trapped in the cycle between the states of "Cognitive Conflict with Confusion" and "Limited Cognitive Engagement." According to these findings, pedagogical insights and analytical implications were addressed. [ABSTRACT FROM AUTHOR] |
| Copyright of Journal of Computing in Higher Education is the property of Springer Nature 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.) | |
| Βάση Δεδομένων: | Complementary Index |
| FullText | Links: – Type: other Text: Availability: 0 CustomLinks: – Url: https://dx.doi.org/doi:10.1007/s12528-025-09460-6 Name: EDS - Springer Nature Journals (s7799221) Category: fullText Text: View record at Springer |
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| Header | DbId: edb DbLabel: Complementary Index An: 194005856 RelevancyScore: 1082 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 1082.4189453125 |
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| Items | – Name: Title Label: Title Group: Ti Data: Exploring the internal dynamics of collaborative engagement in high- and low-performing collaborative problem-solving groups. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Hu%2C+Wanqing%22">Hu, Wanqing</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Yanyan%22">Li, Yanyan</searchLink><br /><searchLink fieldCode="AR" term="%22Huang%2C+Ruiyan%22">Huang, Ruiyan</searchLink><br /><searchLink fieldCode="AR" term="%22Gong%2C+Rushi%22">Gong, Rushi</searchLink><br /><searchLink fieldCode="AR" term="%22Li%2C+Xin%22">Li, Xin</searchLink> – Name: TitleSource Label: Source Group: Src Data: Journal of Computing in Higher Education; Jun2026, Vol. 38 Issue 2, p704-731, 28p – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Group+problem+solving%22">Group problem solving</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Hidden+Markov+models%22">Hidden Markov models</searchLink><br /><searchLink fieldCode="DE" term="%22Social+interaction%22">Social interaction</searchLink><br /><searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br /><searchLink fieldCode="DE" term="%22Attention%22">Attention</searchLink><br /><searchLink fieldCode="DE" term="%22Participation%22">Participation</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Revealing the dynamics of collaborative engagement and its association with collaborative problem-solving (CPS) outcomes is beneficial for guiding teachers to provide adaptive support during CPS processes. Although existing research has indicated that the various dimensions of collaborative engagement influence each other over time, it remains unclear how the internal dynamics of collaborative engagement are associated with CPS outcomes. Additionally, previous studies have primarily employed manual coding to analyze collaborative engagement, which limits the exploration of the dynamic characteristics of collaborative engagement in large-scale datasets. This study aimed to investigate the internal dynamics of collaborative engagement, encompassing behavioral, cognitive, socio-emotional engagement, and their relationship with CPS outcomes. Specifically, a learning analytics method based on deep learning models was employed for the automatic detection of collaborative engagement. Subsequently, according to the results of automated detection, hidden Markov modeling was utilized to investigate the difference in the internal dynamics of collaborative engagement between high- and low-performing groups. These investigations were grounded in a dataset comprising 57,400 utterances collected from 20 groups participating in a CPS activity within a university setting. The findings showed that compared with high-performing groups, low-performing groups were more likely to become stuck in the states of "Limited Cognitive Engagement" and "Lone Neutral Participation" during CPS process. Furthermore, high-performing groups were found to transition away from the collaborative engagement state of "Cognitive Conflict with Confusion" by enhancing behavioral engagement and deepening cognitive engagement. While the low-performing groups remained trapped in the cycle between the states of "Cognitive Conflict with Confusion" and "Limited Cognitive Engagement." According to these findings, pedagogical insights and analytical implications were addressed. [ABSTRACT FROM AUTHOR] – Name: Abstract Label: Group: Ab Data: <i>Copyright of Journal of Computing in Higher Education is the property of Springer Nature 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: Identifiers: – Type: doi Value: 10.1007/s12528-025-09460-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 28 StartPage: 704 Subjects: – SubjectFull: Group problem solving Type: general – SubjectFull: Cooperation Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Hidden Markov models Type: general – SubjectFull: Social interaction Type: general – SubjectFull: Deep learning Type: general – SubjectFull: Attention Type: general – SubjectFull: Participation Type: general Titles: – TitleFull: Exploring the internal dynamics of collaborative engagement in high- and low-performing collaborative problem-solving groups. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Hu, Wanqing – PersonEntity: Name: NameFull: Li, Yanyan – PersonEntity: Name: NameFull: Huang, Ruiyan – PersonEntity: Name: NameFull: Gong, Rushi – PersonEntity: Name: NameFull: Li, Xin IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 10421726 Numbering: – Type: volume Value: 38 – Type: issue Value: 2 Titles: – TitleFull: Journal of Computing in Higher Education Type: main |
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