Toward capturing divergent collaboration in makerspaces using motion sensors
Information and Learning Sciences
ISSN: 2398-5348
Article publication date: 29 March 2022
Issue publication date: 29 April 2022
Abstract
Purpose
This study aims to uncover divergent collaboration in makerspaces using social network analysis to examine ongoing social relations and sequential data pattern mining to invesitgate temporal changes in social activities.
Design/methodology/approach
While there is a significant body of qualitative work on makerspaces, there is a lack of quantitative research identifying productive interactions in open-ended learning environments. This study explores the use of high frequency sensor data to capture divergent collaboration in a semester-long makerspace course, where students support each other while working on different projects.
Findings
The main finding indicates that students who diversely mix with others performed better in a semester-long course. Additional results suggest that having a certain balance of working individually, collaborating with other students and interacting with instructors maximizes performance, provided that sufficient alone time is committed to develop individual technical skills.
Research limitations/implications
These discoveries provide insight into how productive makerspace collaboration can occur within the framework of Divergent Collaboration Learning Mechanisms (Tissenbaum et al., 2017).
Practical implications
Identifying the diversity and sequence of social interactions could also increase instructor awareness of struggling students and having this data in real-time opens new doors for identifying (un)productive behaviors.
Originality/value
The contribution of this study is to explore the use of a sensor-based, data-driven, longitudinal approach in an ecologically valid setting to understand divergent collaboration in makerspaces. Finally, this study discusses how this work represents an initial step toward quantifying and supporting productive interactions in project-based learning environments.
Keywords
Citation
Chng, E., Seyam, M.R., Yao, W. and Schneider, B. (2022), "Toward capturing divergent collaboration in makerspaces using motion sensors", Information and Learning Sciences, Vol. 123 No. 5/6, pp. 276-297. https://doi.org/10.1108/ILS-08-2020-0182
Publisher
:Emerald Publishing Limited
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