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Embodiment Within Computational Models: Explorations of Agency and Normativity

Mon, April 16, 8:15 to 9:45am, Millennium Broadway New York Times Square, Seventh Floor, Room 7.01

Abstract

Study/Project goals
We are broadly interested in how the body can be leveraged as a resource in helping students engage with computationally supported models (Lindgren & Johnson-Glenberg, 2013) as we work with 1st and 2nd grade students. In particular, we explore the differences between when students develop their own models with freedom and flexibility balanced by normative feedback from the computational system with situations where students interact with a pre-existing, normative model that restricts actions and agency.

Design Intervention & Data
We report on analysis from multiple iterations of STEP—a mixed-reality simulation designed to support students’ exploration of the particulate nature of matter through embodied play (Authors). Students played in the mixed-reality environment by (a) embodying particles and interacting with peers to create states of matter based on their collective speed and distance, and (b) embodying energy wands to give/take away energy from AI-controlled particles (Figure 1).
We draw upon Interaction Analysis (Jordan & Henderson, 1995) to analyze particular moments of interaction to highlight differences in these two types of model interactions.
Students as water particles. This flexible approach is reified in activities where students embody water particles and can act in normative and non-normative ways. Their interactions are tracked by the simulation and displayed with additional information indicating the states of matter they create in their exploration (if they are valid). For instance, as students first explore making water particles they often move their bodies in wiggly-flowing movement (waving arms around), though with the aid of feedback from the system learn that they must adjust their distance (moving closer together) along with their speed (moving slowly) to successfully make liquid together.
Students as energy wands. In contrast, students can also act as energy wands to add / remove energy from AI-controlled particles which behave in a normative manner. For instance, as they enter the tracked space as an energy wand, they might approach a projected lattice of particles which represent the structure of ice. As they add energy by standing nearby, they see the particles immediately begin to speed up and move closer as they form a liquid.

Findings & Conclusion
We see a productive tension between the open-ended approach which supports inquiry and agency, but sometimes does not lead immediately to normative accounts. In contrast, the software driven approach has less flexibility, and thus helps students to see the normative approach but has less agency, sometimes leading to less buy-in. Students’ also draw on their experiences in each approach when engaging in the other. For example, when returning to the open-ended modeling, they often attempt to replicate the patterns they saw in the computationally-driven energy wand simulation. In the full paper, we will explore these common structural tensions, and discuss how the trade-offs are related to student engagement with the target concepts.

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