This project will examine how to integrate machine learning, data science, and physical computing in the context of movement based learning. The project focuses on learning environment design for female high school students who participate in physical education activities such as dance in New York and Colorado. Many of these learners are African American and Latina, populations that are underrepresented in Science, Technology, Engineering and Math (STEM). Researchers will leverage learners' expertise and cultural practices in order to engage them in authentic and personally meaningful computing. The students will learn to create computing systems with programmable electronics worn on the body (physical computing), use those systems to create statistical models of movement and gesture (data science and machine learning), and then apply the models in a digital experiential learning environment. Researchers will work closely with physical education teachers and learners to produce design principles, curricula, new educational technologies, and comparative analyses across contexts. This project is funded by the STEM+Computing (STEM+C) program that supports research and development to understand the integration of computing and computational thinking in STEM learning.
Research questions addressed in this project include: 1) How can physical education be leveraged to build expertise in computing? 2) What are the challenges of integrating computing into physical education practices? and 3) How can we meaningfully assess learning outcomes and dispositional shifts with respect to computing in the context of physical education applications? The research has three phases. Phase I will consist of conducting interviews and observations at three development sites including the non-profit organization STEM From Dance in NYC and two teams in Boulder, Colorado. Phase II will consist of participatory design sessions with physical educators and computing educators to develop a deeper understanding of how physical movement and computing (across the sub-disciplines of machine learning, data science, and physical computing) can complement one another using co-designed physical and computing learning activities. The participatory design and co-design activities will explore how sensing technologies and embodiment affordances can reshape computing education and provide alternative pathways for conceptualizing knowledge and cultivating expertise. Phase III will consist of piloting the integrated physical education and computing curricula across the three sites in NYC and Boulder. The curricula will consist of four 5-week computing modules that will be iterated on within the three development sites. Three of the modules will focus on each of the computing sub-disciplines individually, and the fourth will involve advanced topics integrating the disciplines together. This research will produce curriculum and technology to support learning modules in machine learning, data science, and, physical computing, integrating multiple levels of abstraction across the boundaries of hardware and software (i.e., cyber-physical systems). Through this collaborative inquiry researchers will develop transformative knowledge about how to embed computing into established movement based learning practices, resulting in computing curricula and tools that build on the learners' and educators' authentic practices and needs.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
