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resource project Exhibitions
As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public's engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner's speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The project is funded by the Advancing Informal STEM Learning (AISL) program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. This includes providing multiple pathways for broadening access to and engagement in STEM learning experiences, advancing innovative research on and assessment of STEM learning in informal environments, and developing understandings of deeper learning by participants. The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry's (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy - currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.
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TEAM MEMBERS: Katy Borner Kylie Peppler Bryan Kennedy Stephen Uzzo Joe E Heimlich
resource project Public Programs
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative resources for use in a variety of settings. This proposed effort embraces broad participation by the three Ute tribes, History Colorado, and scientists in the field of archaeology to investigate and integrate traditional ecological knowledge and contemporary Western science. The project will preserve knowledge from the Ute peoples of Colorado and Utah, including traditional technology, ethnobotany, engineering and math. Results from this project will inform educational efforts in similar communities.

This project will build on the long-standing collaborations between History Colorado (HC), the Southern Ute Indian Tribe, Ute Mountain Ute Tribe and Ute Indian Tribe, Uintah & Ouray Reservation, and the Dominguez Archaeological Research Group DARG). HC will implement and evaluate a regional informal learning collaboration focused on Ute traditional and contemporary STEM knowledge serving over 128,000 learners through tribal programs, local history museums and educational networks. This project will advance the understanding of integrated knowledge and the role of Ute people as STEM learners and practitioners. This Informal Science Learning project will increase lifelong STEM learning in rural communities and create a replicable model for collaboration among tribes, history museums, and scientists.
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TEAM MEMBERS: Liz Cook Sheila Goff Shannon Voirol JJ Rutherford