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

As the field of data science education is fastly solidifying, the project team aims to increase knowledge of the community-based critical data practices of youth from non-dominant communities. The team will use these developing empirical understandings of youths’ community-based critical data practices to iteratively construct, collaboratively with youth and community partners, a learning model of youths’ community-based critical practices, grounded in social epistemological perspectives. Learning scientists from the University of Michigan, the University of Washington Seattle, the University of North Carolina Greensboro and the University of Maryland have assembled an interdisciplinary team to accomplish these objectives. These researchers have decades of experiences in participatory methodologies involving youth and community members for the study of equitable and consequential learning in STEM and everyday life.

Using abductive analysis and theory building, there will be three cycles of design: 1) Building a learning model of Community-Based Critical Data Engagement using extant NSF RAPID study data of 480 hours of individual interview and experience sampling; 2) Test and refine the model by “challenging” it in other relevant contexts; 3) Use model to work further refine and build out design principles and future research directions.