Trajectories of Learning Embodied Interaction in Change
Abstract: This dissertation is about learning as changing understanding in social and situated activities. It takes part in the development of a reconceptualization of learning initiated within participationist perspectives. Multiparty interaction in situated activities is a primordial site for the exploration of human action and cognition. Through the theoretical framework of Conversation Analysis (CA), a method for the analysis and description of trajectories of learning is proposed. Departing from a view of learning, interaction, and cognition as closely related, learning is argued as gradually changing understanding in situated activities.The empirical material consists of video recordings from an elementary school and pilot training. The recordings are analyzed using CA methods, including detailed attention to embodied features of interaction.The analyses focus the development of trajectories of learning through the participants’ orientations. The trajectories are based on topicalizations and co-constructions of contents of learning, where interactional organization and content are interrelated. Participants are shown to make relevant relations between past, present, and future actions and material settings, and their ways of aligning and resisting participation and change are explored. A framework for the analysis of learning as embodied interaction in change is developed.The dissertation shows the fruitfulness of CA work for the understanding of learning processes. The results underline the importance of including embodied action, as constitutive of the co-constructions of contents, into learning studies. The value of highlighting learning as co-construction and of anchoring the analyses in the participants’ orientations is underscored. The results further the understanding of how people learn, and of how they make relevant knowledge and experiences in activity. The understanding of learning and change as action, which can be initiated, aligned with and resisted, opens up for future developments within CA, where learning researchers might be able to describe more precisely how human learning is constituted.
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