Exploring collaborative learning in makerspaces through a complex systems lens: Aligning theory and method for deeper insights

Abstract Makerspaces are complex systems comprising a range of elements and participants, including materials, learners, and educators. Examining collaborative learning through the lens of complex systems theory provides new ways to identify challenges and opportunities within educational makerspaces. This can be achieved through social science methods that allow researchers to investigate different levels of participation in learning activities, including individual behaviours, small‐group interactions, and sociotechnical networks. To this end, we present a mixed‐methods approach for analysing group interactions and network‐level properties using video data from classroom observations. Our methodology combines interaction analysis with sociotechnical network analysis. The primary challenge identified is the rapid increase in complexity during making activities, which hinders full participation. This is evident in the nested structures revealed by the network analysis and in the lack of shared understanding shown by the interaction analysis. We developed a conceptual framework that enables researchers to analyse collaborative learning in makerspaces. Practical implications include science learning goals that consider making and scaffolding practices that leverage pseudo‐concepts. Practitioner notes What is already known about this topic Makerspaces are increasingly used as learning arenas for developing science, technology, engineering, and mathematics (STEM) knowledge and skills in school. However, researchers and educators face challenges in understanding and balancing makerspace pedagogy with traditional school learning. A complex systems perspective is widely applied across many fields, and educational scientists have developed eight qualities of complex systems that are relevant to makerspace activities. These provide a theoretical foundation for analysing the dynamics of collaborative learning. What this paper adds This paper combines a complex systems perspective with a new mixed‐methods approach to offer a fine‐grained analysis of learning activities in a makerspace. By focusing on the specific qualities of makerspaces—such as creativity, collaboration, and the use of tools and materials—we contribute to the field by shedding light on how collaborative learning, as a process that fosters shared understanding, unfolds at different levels within these environments. Implications for practice and/or policy There may be a need to re‐evaluate the success criteria for makerspaces as educational environments. Instead of prioritizing the creation of functioning or advanced artefacts, greater emphasis could be placed on activity‐focused criteria that recognize the interplay between domain‐specific concepts and domain‐general skills involved in managing complex systems.