Building a Learning Model of Youths' Community-Based Critical Data Practices

The primary objective of this study is to develop, test and refine a model to describe how youth develop knowledge within their communities using critical data practices. Critical data practices include what youth do with, in relation to, and oriented around data to learn about their world and solve new problems. For example, throughout the COVID-19 pandemic, youth have engaged with data such as local COVID-19 dashboards for their schools and cities, visualizations of viral spread, and social media describing mental health strategies for coping with long-term isolation. This study is intended to produce insights on how learning with and about data is shaped by equity concerns, cultural, and contextual factors. This model will be grounded in the analysis of existing data sets from Michigan, Washington, North Carolina, Tennessee, and Maryland focused on youths' engagement with data. Youth, families, and educators from non-dominant communities in Michigan and Washington will co-analyze data to create a model of data practices together with women learning scientists from diverse racial backgrounds and geographic locations. In addition, project participants will collaboratively contribute to the development of a set of principles to guide the design of learning environments focused on critical data engagement. The project team aims to understand how people make sense of, navigate, critique and transform data towards empowered meaning-making that can inform personal decisions and actions. This approach will advance how the field understands both data literacies and people's reasons for engaging and disengaging with data. Project findings will contribute to supporting the design of new and equitable learning experiences in the data sciences in support of broadening participation.

This project will build inferences to support explanations across existing datasets in three design-based research cycles using participatory approaches to theory building. The first design cycle will draw upon empirical understandings of the community-based critical data practices of youth and families from two non-dominant communities in the Midwest and West Coast during the COVID-19 pandemic. Later cycles will focus on existing data from three additional youth-based projects from the East Coast and two cities in the Southern US. Collaborative examination of data with researchers, youth and community partners will produce a learning model of youths' community-based critical practices, grounded in social, cultural, racial, ethical, and political perspectives on knowledge building. There is an urgent need for the field to understand how people learn science with a large and often confusing volume of data in order to make decisions that have direct impact on everyday living and community well-being. This project also includes the voices of youth, families, and educators from non-dominant communities in the model-building process. This project will yield recommendations for how the field may better identify, acknowledge, and support youths' efforts to engage critically with data and data-rich technologies as a part of ongoing, out-of-school STEM learning and development. The project will be led by researchers at the University of Michigan and will include a diverse and interdisciplinary team of learning scientists from the Universities of Michigan, Washington (Seattle), North Carolina (Greensboro) and Maryland (College Park).

This project is funded by the EHR Core Research (ECR) program, which supports work that advances fundamental research on STEM learning and learning environments, broadening participation in STEM, and STEM workforce development.

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.