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Charting the Development of Collaboration Skills through Collaborative Learning Analytics Systems

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Title: Charting the Development of Collaboration Skills through Collaborative Learning Analytics Systems
Language: English
Authors: Xiaomeng Huang (ORCID 0000-0002-6992-061X), Xavier Ochoa (ORCID 0000-0002-4371-7701)
Source: Journal of Learning Analytics. 2025 12(1):338-366.
Availability: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
Peer Reviewed: Y
Page Count: 29
Publication Date: 2025
Document Type: Journal Articles
Information Analyses
Descriptors: Learning Analytics, Cooperative Learning, Cooperation, Skill Development, Feedback (Response), Automation, Literature Reviews
ISSN: 1929-7750
Abstract: Collaboration skills are fundamental to effective collaborative learning, career success, and responsible citizenship. Collaborative learning analytics (CLA) systems hold significant potential in helping students develop these skills by automatically collecting group interaction data, analyzing skill levels, and providing actionable feedback so students can reflect, practise, and improve. Previously, most collaborative feedback systems have focused on improving collaborative processes rather than serving as instructional systems for developing collaboration skills over time. To identify what is needed to navigate toward this new type of tool, our paper proposes an interdisciplinary framework that serves as a guiding compass for designing and evaluating such systems. Through an extensive literature review, we evaluate 15 selected systems through the lens of each element of this framework. We map out the current state of the field and identify four major gaps that need to be addressed to transition from systems that support collaboration to systems that support the development of collaboration skills. These gaps are unexplored collaboration skills, lack of validated indicators, limited modelling techniques, and pedagogical feedback design. Finally, we propose a set of corresponding research agendas to bridge these gaps, providing a forward-looking roadmap for designing effective and actionable CLA systems for collaboration skills development.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1465737
Database: ERIC
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  Data: Charting the Development of Collaboration Skills through Collaborative Learning Analytics Systems
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  Data: <searchLink fieldCode="AR" term="%22Xiaomeng+Huang%22">Xiaomeng Huang</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-6992-061X">0000-0002-6992-061X</externalLink>)<br /><searchLink fieldCode="AR" term="%22Xavier+Ochoa%22">Xavier Ochoa</searchLink> (ORCID <externalLink term="https://orcid.org/0000-0002-4371-7701">0000-0002-4371-7701</externalLink>)
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  Data: <searchLink fieldCode="SO" term="%22Journal+of+Learning+Analytics%22"><i>Journal of Learning Analytics</i></searchLink>. 2025 12(1):338-366.
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  Data: Society for Learning Analytics Research. 121 Pointe Marsan, Beaumont, AB T4X 0A2, Canada. Tel: +61-429-920-838; e-mail: info@solaresearch.org; Web site: https://learning-analytics.info/index.php/JLA/index
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  Data: 29
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  Data: Journal Articles<br />Information Analyses
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  Data: <searchLink fieldCode="DE" term="%22Learning+Analytics%22">Learning Analytics</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperative+Learning%22">Cooperative Learning</searchLink><br /><searchLink fieldCode="DE" term="%22Cooperation%22">Cooperation</searchLink><br /><searchLink fieldCode="DE" term="%22Skill+Development%22">Skill Development</searchLink><br /><searchLink fieldCode="DE" term="%22Feedback+%28Response%29%22">Feedback (Response)</searchLink><br /><searchLink fieldCode="DE" term="%22Automation%22">Automation</searchLink><br /><searchLink fieldCode="DE" term="%22Literature+Reviews%22">Literature Reviews</searchLink>
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  Data: Collaboration skills are fundamental to effective collaborative learning, career success, and responsible citizenship. Collaborative learning analytics (CLA) systems hold significant potential in helping students develop these skills by automatically collecting group interaction data, analyzing skill levels, and providing actionable feedback so students can reflect, practise, and improve. Previously, most collaborative feedback systems have focused on improving collaborative processes rather than serving as instructional systems for developing collaboration skills over time. To identify what is needed to navigate toward this new type of tool, our paper proposes an interdisciplinary framework that serves as a guiding compass for designing and evaluating such systems. Through an extensive literature review, we evaluate 15 selected systems through the lens of each element of this framework. We map out the current state of the field and identify four major gaps that need to be addressed to transition from systems that support collaboration to systems that support the development of collaboration skills. These gaps are unexplored collaboration skills, lack of validated indicators, limited modelling techniques, and pedagogical feedback design. Finally, we propose a set of corresponding research agendas to bridge these gaps, providing a forward-looking roadmap for designing effective and actionable CLA systems for collaboration skills development.
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      – Text: English
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        StartPage: 338
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      – SubjectFull: Learning Analytics
        Type: general
      – SubjectFull: Cooperative Learning
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      – SubjectFull: Cooperation
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      – SubjectFull: Skill Development
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      – TitleFull: Charting the Development of Collaboration Skills through Collaborative Learning Analytics Systems
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