From Data To Awesome (D2A): Youth Learning to be Data Scientists

From Data To Awesome (D2A): Youth Learning to be Data Scientists


More than 300 underserved Bay Area young people annually are motivated and prepared to discover, utilize and share data science skills as they produce high-impact interactive media for local communities and national audiences.


Increasingly, the STEM workforce requires knowledge of data science and data analysis including representations of data for public use such as in media outlets. The project will be a partnership between a non-profit organization and a university in order to engage youth in data science projects as they produce interactive media for local and national audiences. The technology platform, Local Ground, allows youth to collect, map and visualize data. The model will engage students in hands-on, interactive data science projects grounded in questions that are meaningful to the students and their communities. The setting for the program will be an after-school program taught by peer-educators and faculty. It will work with approximately 300 youth from populations typically underrepresented in STEM and STEM-related disciplines and low-income students. The project will also develop and share toolkits for other educators using data analysis projects in their in-school and after-school programs. This project will advance efforts of the Innovative Technology Experiences for Students and Teachers (ITEST) program to better understand and promote practices that increase students' motivations and capacities to pursue careers in fields of science, technology, engineering, or mathematics (STEM).

The research questions will focus on scaffolds for supporting youth in data science projects, how students embed data-driven argumentation in their writing, and how they apply STEM content knowledge through creating data-rich interactive media. The investigators will also inquire about barriers to engagement in data science for young people traditionally marginalized in STEM professions and how tools can support such students as they learn about data analysis and visualization. The project will use an iterative design-based research model to understand how youth develop their data science presentations and products and how the data science projects develop content knowledge. They will collect data about youth interaction with the technology using audio and video recording, projects and data collected by the youth, and interviews.


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1513282, 1513296 & 1646690


2015 - 2018



Youth Radio Oakland, CA
Cornell University Ithaca, NY

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