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DCLM framework: understanding collaboration in open-ended tabletop learning environments

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International Journal of Computer-Supported Collaborative Learning Aims and scope Submit manuscript

Abstract

There is a growing understanding of the unique ways that tabletops support effective collaboration; however, this research mostly focuses on environments in which learners work towards a single shared goal. Underpinning this perspective, either implicitly or explicitly, is the theory that collaborative learning is a process of attaining convergent conceptual change. However, this model of collaboration may not apply to all scenarios where learners are working together. In particular, informal, open-ended exploratory environments support (and often promote) shared activities where the goal may not be for all participants to emerge with a single, shared understanding. There is increased interest in understanding the efficacy of designs that support (and encourage) learners to collaborate while seeking divergent goals, ideas, and conceptions. This paper advances a framework (Divergent Collaboration Learning Mechanisms - DCLM) for recognizing and coding collaboration and divergent learning in such environments. We apply the DCLM framework to an informal tabletop environment (Oztoc) as a means of highlighting how DCLM may reveal new productive interactions environments that support divergent forms of collaboration, mentorship, and learning. Analysis of participants’ interactions within Oztoc revealed that participants who have non-aligned goals can still productively collaborate, and in many cases can provide insight and feedback that would not be possible in shared-goal activities. We conclude with an examination of how open-ended exploratory environments can support communities of practice and legitimate peripheral participation, and the importance of divergent inquiry and divergent conceptual change across a range of learning environments.

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Tissenbaum, M., Berland, M. & Lyons, L. DCLM framework: understanding collaboration in open-ended tabletop learning environments. Intern. J. Comput.-Support. Collab. Learn 12, 35–64 (2017). https://doi.org/10.1007/s11412-017-9249-7

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