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For the past several years, our team has been developing and implementing curriculum and tools for integrating sports in data science learning experiences. We have developed a collection of activities and tools that we are eager to share with the larger data science community. The purpose of this session is to give participants an opportunity to explore some of these tools and activities in community with other data science educators and learners. The workshop will also serve as a space to try designing some new activities and ideate future directions for this work within the specific contexts of each workshop participant. Hence, we propose this workshop as a set of resources and a community that might spur new ideas that participants can adapt and extend based on the goals, needs, and affordances of their respective contexts. 

With increasing understanding of the inextricable connections between learners and the tools that facilitate their learning within complex social systems, new theoretical and methodological developments have emerged to allow us to explore the materiality in learning environments. Sociomateriality (Fenwick, 2015) urges us to consider the interdependence of social and material elements in learning. Rather than viewing classroom spaces and educational tools as static, inert material objects, sociomateriality posits them as capable of exerting force by the way they are acted on or by. This approach has the potential to help respond to the global crises by interrogating and recoupling learning and knowledge with networks and the power relationships inherent in all learning. To this end, this symposium aims to bring researchers together around a common theme of unpacking how sociomateriality might be used as a theoretical foundation or analytical approach for Learning Sciences research. 

Culturally relevant and sustaining implementations of computing education are increasingly leveraging young learners' passion for sports as a platform for building interest in different STEM (Science, Technology, Engineering, and Math) concepts. Numerous disciplines spanning physics, engineering, data science, and especially AI based computing are not only authentically used in professional sports in today's world, but can also be productively introduced to introduce young learnres to these disciplines and facilitate deep engagement with the same in the context of sports. In this work, we present a curriculum that includes a constellation of proprietary apps and tools we show student athletes learning sports like basketball and soccer that use AI methods like pose detection and IMU-based gesture detection to track activity and provide feedback. We also share Scratch extensions which enable rich access to sports related pose, object, and gesture detection algorithms that youth can then tinker around with and develop their own sports drill applications. We present early findings from pilot implementations of portions of these tools and curricula, which also fostered discussion relating to the failings, risks, and social harms associated with many of these different AI methods – noticeable in professional sports contexts, and relevant to youths' lives as active users of AI technologies as well as potential future creators of the same. 

Science museums are often interactive spaces where a variety of visitors engage with exhibits in diverse ways. While trying to support participants? behavior in ways that make intuitive sense for these behaviors in a museum context, these exhibits need to support interests and participation in forms that are meaningfully diverse - to make domains accessible to learners belonging to groups minoritized in those domains. In this paper, we present an interactive computational thinking exhibit designed to foster a multiplicity of goals and participatory behaviors. We also present preliminary analysis on how we can use play data to delineate the pursuit of different goals mediated through different pursuits. We also find care to be a uniquely valuable aesthetic motivator in gameplay, often overlooked in common design frameworks - with potential to expand perspectives on computing and combat inequity among computing learners. 

This paper describes the design of a collaborative game, called Rainbow Agents, that has been created to promote computational literacy through play. In Rainbow Agents, players engage directly with computational concepts by programming agents to plant and maintain a shared garden space. Rainbow Agents was designed to encourage collaborative play and shared sense-making from groups who are typically underrepresented in computer science. In this paper, we discuss how that design goal informed the mechanics of the game, and how each of those mechanics affords different goal alignments towards gameplay (e.g. competitive versus collaborative). We apply this framework using a case from an early implementation, describing how player goal alignments towards the game changed within the course of a single play session. We conclude by discussing avenues of future work as we begin data collection in two heavily diverse science museum locations.