Overview: Every year more than 20 million youth participate in athletics. While referred to as scholar-athletes, athletics and academics are frequently viewed as conflicting interests. A disproportionate number of scholar-athletes are underrepresented minorities, many of whom have been socialized into pursuing athletics as an ideal path to social mobility. For student-athletes of color, the scholar-athlete conflict is magnified by stereotypes that African Americans are innately superior athletes and inferior intellects. Not surprisingly, then, youth student-athletes are seldom encouraged to explore opportunities for athletics to promote embodied Science, Technology, Engineering, Mathematics and Computing (STEM+C) learning, and for STEM+C to enable improved athletic performance. A primary goal of this proposal, building on the PIs current research and prior experience as a collegiate student-athlete, is to disrupt this dichotomy by developing the pedagogical, technological and analytic tools needed to support athletic-centered computer science learning experiences. The pedagogical tools include novel learning activities that bridge data science, engineering design, machine learning and physical computing with athletics. The activites are co-designed with student-athletes, coaches and after-school program providers, and extend the PI’s pilot work on a program called Data in Motion. The technological tools include age-appropriate and accessible interfaces for collecting, visualizing and drawing inferences from the multimodal data that students collect. Technological tools also include supporting students in designing and building custom wearables using physical computing. Finally, the analytic tools leverage the PI’s expertise in Multimodal Learning Analytics (MMLA) to extend and scale elements of the STEM Activation Framework’s Observation and Engagement Protocol via automated feature extraction and data annotation. This project uses an iterative, user-centered and participatory design model to realize the project goals. Interviews and observations of student-athletes and coaches will be conducted in Year 1 to better understand their knowledge and perceptions of computer science, and identify opportunities to integrate physical computing with sports practices. Year 2 includes the development and integration of multimodal data collection and data visualization tools that reflect the needs of student-athletes and coaches. Year 3 involves adding support for more complex inferencing through multimodal fusion and machine learning. Year 4 focuses on implementation of MMLA techniques for characterizing student learning during these activities. This is particularly important given the need to assess learning in non-traditional learning environments. Year 5 focuses on data analysis and dissemination. To complement these technological developments, the PI will create and teach a new course on Sports, Wearables and Learning and host summer programs for student-athletes where they use physical computing to study and improve their athletic performance. He will host summer coach and educator workshops to support activity co-design and prepare coaches and educators to incorporate computing into their teams’ activities. Intellectual Merit. This project investigates the development and evaluation of a model for engaging students in data science and physical computing experiences that could potentially provide a novel pathway to the pursuit of STEM+C careers, especially for underrepresented students. It also provides best practices in the design a
|Effective start/end date||6/15/21 → 5/31/26|
- National Science Foundation (CNS-2047693-001)
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