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Cross-community knowledge building with idea thread mapper

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Abstract

Research on computer-supported collaborative learning faces the challenge of extending student collaboration to higher social levels and enabling cross-boundary interaction. This study investigated collaborative knowledge building among four Grade 5 classroom communities that studied human body systems with the support of Idea Thread Mapper (ITM). While students in each classroom collaborated in their local (home) discourse space to investigate various human body functions, they generated reflective syntheses— “super notes”—to share knowledge progress and challenges in a cross-community meta-space. As a cross-community collaboration, students from the four classrooms further used the Super Talk feature of ITM to investigate a common problem: how do people grow? Data sources included classroom observations and videos, online discourse within each community, students’ super notes and records of Super Talk discussion shared across the classrooms, and student interviews. The results showed that the fifth-graders were able to generate high quality super notes to reflect on their inquiry progress for cross-classroom sharing. Detailed analysis of the cross-classroom Super Talk documented students’ multifaceted understanding constructed to understand how people grow, which built on the diverse ideas from each classroom and further contributed to enriching student discourse within each individual classroom. The findings are discussed focusing on how to approach cross-community collaboration as an expansive and dynamic context for high-level inquiry and continual knowledge building with technology support.

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Availability of data and material

Following our IRB protocol, the data of this study cannot be made openly available. Those who are interested to access the detailed coding frameworks and data samples can send their requests to the authors.

Code availability

The Idea Thread Mapper (ITM) software is freely available to educators and students for classroom use. Developers who are interested to create or integrate software tools inter-operatable with ITM are welcome to reach out to the authors about their needs.

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Acknowledgements

This research was sponsored by the US National Science Foundation (#1441479). Any opinions and conclusions expressed in this article are those of the authors and do not necessarily reflect the views of the National Science Foundation. Part of the research findings have been presented at a conference hosted by the International Society of the Learning Sciences. We owe special thanks to the teachers and students for their creative work enabling this research, and to our research team members who contributed to the technology development and classroom research. We also extend our gratitude to Allan Collins, Keith Sawyer, Marlene Scardamalia, and Gerry Stahl for their advisory input, and to the journal editors and reviewers for their suggestions.

Funding

This study is sponsored by the US National Science Foundation through the Cyberlearning and Future Learning Technologies program and the Discovery Research K-12 program (#1441479).

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Authors and Affiliations

  1. National Institute of Education, Nanyang Technological University, Singapore, Singapore

    Guangji Yuan

  2. Department of Educational Theory and Practice, University at Albany, SUNY, USA

    Jianwei Zhang

  3. Department of Computer Science, University at Albany, SUNY, USA

    Mei-Hwa Chen

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  1. Guangji Yuan
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Correspondence to Guangji Yuan.

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The authors have no conflicts of interest in the work reported in this study.

Ethics approval

This study was conducted in compliance with the research protocol approved by the University at Albany IRB (IRB #14-E-140).

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Following the IRB protocol, this study obtained signed consent forms from all the research participants, including each student participant (through child assent and parent/guardian consent) and teacher.

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We, Guangji Yuan, Jianwei Zhagn, Mei-Hwa Chen, give our consent for the publication of identifiable details, which can include photograph(s) and/or videos and/or case history and/or details within the text to be published in International Journal of Computer-Supported Collaborative Learning.

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Yuan, G., Zhang, J. & Chen, MH. Cross-community knowledge building with idea thread mapper. Intern. J. Comput.-Support. Collab. Learn 17, 293–326 (2022). https://doi.org/10.1007/s11412-022-09371-z

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