Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics.
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| Title: | Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. |
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| Authors: | Nguyen, Andy1 andy.nguyen@oulu.fi, Järvelä, Sanna1, Rosé, Carolyn2, Järvenoja, Hanna1, Malmberg, Jonna1 |
| Source: | British Journal of Educational Technology. Jan2023, Vol. 54 Issue 1, p293-312. 20p. 4 Charts, 3 Graphs. |
| Subject Terms: | *Collaborative learning, *Educational technology, *Data analysis, *Artificial intelligence, *Science education (Secondary), *Teenagers, *Secondary education, *Learning analytics, Process mining |
| Abstract: | Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace‐based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace‐based approach to study SSRL, there remains a paucity of evidence on how trace‐based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace‐based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace‐based data could be utilised to predict learning outcomes in collaborative learning. Practitioner notesWhat is already known about this topic Socially shared regulation has been recognised as an essential aspect of collaborative learning success.It is challenging to make the processes of learning regulation 'visible' to better understand and support student learning, especially in dynamic collaborative settings.Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.What this paper adds Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.Implications for practice and/or policy Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation. [ABSTRACT FROM AUTHOR] |
| Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell 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 |
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| Header | DbId: ehh DbLabel: Education Research Complete An: 161587251 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Nguyen%2C+Andy%22">Nguyen, Andy</searchLink><relatesTo>1</relatesTo><i> andy.nguyen@oulu.fi</i><br /><searchLink fieldCode="AR" term="%22Järvelä%2C+Sanna%22">Järvelä, Sanna</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Rosé%2C+Carolyn%22">Rosé, Carolyn</searchLink><relatesTo>2</relatesTo><br /><searchLink fieldCode="AR" term="%22Järvenoja%2C+Hanna%22">Järvenoja, Hanna</searchLink><relatesTo>1</relatesTo><br /><searchLink fieldCode="AR" term="%22Malmberg%2C+Jonna%22">Malmberg, Jonna</searchLink><relatesTo>1</relatesTo> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22British+Journal+of+Educational+Technology%22">British Journal of Educational Technology</searchLink>. Jan2023, Vol. 54 Issue 1, p293-312. 20p. 4 Charts, 3 Graphs. – Name: Subject Label: Subject Terms Group: Su Data: *<searchLink fieldCode="DE" term="%22Collaborative+learning%22">Collaborative learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Educational+technology%22">Educational technology</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+analysis%22">Data analysis</searchLink><br />*<searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br />*<searchLink fieldCode="DE" term="%22Science+education+%28Secondary%29%22">Science education (Secondary)</searchLink><br />*<searchLink fieldCode="DE" term="%22Teenagers%22">Teenagers</searchLink><br />*<searchLink fieldCode="DE" term="%22Secondary+education%22">Secondary education</searchLink><br />*<searchLink fieldCode="DE" term="%22Learning+analytics%22">Learning analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Process+mining%22">Process mining</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Socially shared regulation contributes to the success of collaborative learning. However, the assessment of socially shared regulation of learning (SSRL) faces several challenges in the effort to increase the understanding of collaborative learning and support outcomes due to the unobservability of the related cognitive and emotional processes. The recent development of trace‐based assessment has enabled innovative opportunities to overcome the problem. Despite the potential of a trace‐based approach to study SSRL, there remains a paucity of evidence on how trace‐based evidence could be captured and utilised to assess and promote SSRL. This study aims to investigate the assessment of electrodermal activities (EDA) data to understand and support SSRL in collaborative learning, hence enhancing learning outcomes. The data collection involves secondary school students (N = 94) working collaboratively in groups through five science lessons. A multimodal data set of EDA and video data were examined to assess the relationship among shared arousals and interactions for SSRL. The results of this study inform the patterns among students' physiological activities and their SSRL interactions to provide trace‐based evidence for an adaptive and maladaptive pattern of collaborative learning. Furthermore, our findings provide evidence about how trace‐based data could be utilised to predict learning outcomes in collaborative learning. Practitioner notesWhat is already known about this topic Socially shared regulation has been recognised as an essential aspect of collaborative learning success.It is challenging to make the processes of learning regulation 'visible' to better understand and support student learning, especially in dynamic collaborative settings.Multimodal learning analytics are showing promise for being a powerful tool to reveal new insights into the temporal and sequential aspects of regulation in collaborative learning.What this paper adds Utilising multimodal big data analytics to reveal the regulatory patterns of shared physiological arousal events (SPAEs) and regulatory activities in collaborative learning.Providing evidence of using multimodal data including physiological signals to indicate trigger events in socially shared regulation.Examining the differences of regulatory patterns between successful and less successful collaborative learning sessions.Demonstrating the potential use of artificial intelligence (AI) techniques to predict collaborative learning success by examining regulatory patterns.Implications for practice and/or policy Our findings offer insights into how students regulate their learning during collaborative learning, which can be used to design adaptive supports that can foster students' learning regulation.This study could encourage researchers and practitioners to consider the methodological development incorporating advanced techniques such as AI machine learning for capturing, processing and analysing multimodal data to examine and support learning regulation. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of British Journal of Educational Technology is the property of Wiley-Blackwell 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: 10.1111/bjet.13280 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 20 StartPage: 293 Subjects: – SubjectFull: Collaborative learning Type: general – SubjectFull: Educational technology Type: general – SubjectFull: Data analysis Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Science education (Secondary) Type: general – SubjectFull: Teenagers Type: general – SubjectFull: Secondary education Type: general – SubjectFull: Learning analytics Type: general – SubjectFull: Process mining Type: general Titles: – TitleFull: Examining socially shared regulation and shared physiological arousal events with multimodal learning analytics. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Nguyen, Andy – PersonEntity: Name: NameFull: Järvelä, Sanna – PersonEntity: Name: NameFull: Rosé, Carolyn – PersonEntity: Name: NameFull: Järvenoja, Hanna – PersonEntity: Name: NameFull: Malmberg, Jonna IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 01 Text: Jan2023 Type: published Y: 2023 Identifiers: – Type: issn-print Value: 00071013 Numbering: – Type: volume Value: 54 – Type: issue Value: 1 Titles: – TitleFull: British Journal of Educational Technology Type: main |
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