Designing Learning Technology Environments for Middle School Students to Analyze and Interpret Data Visualizations

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We live in a digital age where data are continuously generated and used to help us understand and appreciate our world. Data visualizations have become a tool for understanding data, revealing hidden structures, patterns, and social movements to understand our world in new ways. Yet, many individuals have difficulties interpreting and working with data visualizations, thus, opportunities are needed to develop skills related to reading, communicating, and comprehending data visualizations appropriately. This project aims to develop and implement a curricular program for middle-school-aged youth in community-based settings to develop data visualization skills through technologies in the context of socio-scientific issues. The overarching goal is to support middle-grade youth in developing data visualization skills that not only allow them to become critical public consumers of data but also provide them with the ability to understand data. The curricular program will provide culturally relevant experiences that may motivate youth to pursue STEM majors and careers, potentially broadening participation in STEM. Data visualizations have become a ubiquitous tool for understanding the multitude of data continuously generated. Accordingly, the ability to understand and use data visualizations has become crucial not only for professional data scientists but also for everyday citizens. This project integrates data visualization practices within STEM education research and sociocultural learning theories to build new theoretical understandings about the nature of data visualization learning in the context of pressing socio-scientific issues. The research goals of this project are to provide empirical evidence of middle school students building data visualization practices through learning technologies, and to develop a theoretical learning model for developing data visualization practices in youth. Drawing on Integrated STEM education and Experiential Learning Theory, the students’ learning experiences will also build on the existing technical, programmatic, and social infrastructures that support informal learning opportunities that are culturally relevant. The project will create educational STEM ecosystems that promote the accumulation of non-traditional STEM experiences that may enable multiple pathways to STEM degrees and careers. Further, materials for the data science learning environment will be made openly available and easily adaptable to both informal or formal education settings.This project is co-funded by the STEM Education Postdoctoral Research Fellowship (STEM Ed PRF) program and by the Innovative Technology Experiences for Students and Teachers (ITEST) program. The STEM Ed PRF program aims to enhance the research knowledge, skills, and practices of recent doctorates in STEM, STEM education, education, and related disciplines to advance their preparation to engage in fundamental and applied research that advances knowledge within the field. The ITEST program supports projects that build understandings of practices, program elements, contexts, and processes contributing to increasing students' knowledge and interest in science, technology, engineering, and mathematics (STEM) and information and communication technology (ICT) careers.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.