Embodied Interaction Analysis

• In Event: What's Next for Interaction Analysis of Learning? Aligning Analytical Approaches With Theoretical Turns

Abstract

This paper brings together the perspectives of two groups of collaborators who explored the role of analysts’ bodies in IA. Our independent and intersecting work is motivated by the idea that analysis is a learning process; thus new analytic approaches and techniques can expand our conceptions of learning. Across our projects, we have collaborated with researchers and artists (dance & theater) to engage in a wide range of embodied forms of IA such as viewing interactions as choreography, creating new choreographies from movements in the record, and transcribing interactions as movement scores (Figure 1).
Situated historically during a sociocultural (Lave & Wenger, 1991) turn in the Learning Sciences, Jordan & Henderson's (1995) seminal IA text expresses commitment to the analytic capacity of collaborating groups. Since then, theories of embodied learning have matured and the Learning Sciences have attended increasingly to "Learning on the Move" (Authors, 2020). In contrast, our methods for studying interaction have remained sedentary. Although Erickson (2004) expanded the scene of analysis to include choral readings of transcripts, using analysts' voices to highlight the sequential organization of talk-in-interaction (Authors, 2018), analysts' bodies have not been incorporated explicitly into IA protocols.
Just as there is no "disembodied" cognition, there is no IA for which the body is not a resource. Analysts regularly recruit their bodies to mimic and structurally model recorded interactions (Authors, in preparation). We have taken this a step further to conceptualize IA processes that place analysts' bodies in individual and coordinated motion to respond to video data. The following commitments form the foundation of our work: (1) physically enacting movements positions the analyst for new forms of noticing (Kirsh, 2010); (2) artistic practices provide models for ensemble building, framing movement as generative responses to ideas and other "propositions" (Author, 2020b; Author, 2021b; Authors, in preparation); and (3) nature-culture relations provide models for understanding movement as co-constituted through relations between humans and more-than-humans (Authors, in preparation). Integrating expanding conceptions of learning into our analytic practices, we have iteratively refined embodied IA methods that begin from the premise that analysts can individually and collectively enact observed movements to make sense of them (Authors, 2019; Author, 2019; Authors 2019; Authors, 2020; Authors, 2021b; Authors, in preparation).
Across our work we have designed multiple practices to support embodied IA. These practices leverage analysts’ felt experiences of the sequential organization of multi-modal interaction and have facilitated analysts’ own understandings about how ideas, propositions, and knowledge are expressed through the coordination of bodies (human and more-than-human) in places. We conceptualize these embodied IA practices as populating a three-dimensional space (Figure 1) which accounts for the expansiveness of movement, timing of the enactments, and number of movers. Engaging in these practices made important non-verbal components of interaction accessible to analysts in new ways, as these movements became publicly visible and manipulable all at once. Physically enacting noticings amplified important relations between interacting humans, more-than-humans, and material components of the environment, emphasizing how learning-in-interaction involves a complex coordination of many moving parts.

Authors

• Lauren Vogelstein, New York University

• Lindsay E. Lindberg, University of California - Los Angeles

• Corey Brady, Southern Methodist University

• Ananda M. Marin, University of California - Los Angeles

• Rogers P. Hall, Vanderbilt University

• Rebecca Steinberg

• Curtis Thomas

• Elisa Noemí Orellana Faulstich