Sense-making with data through the process of visualization—recognizing and constructing meaning with these data—has been of interest to learning researchers for many years. Results of a variety of data visualization projects in museums and science centers suggest that visitors have a rudimentary understanding of and ability to interpret the data that appear in even simple data visualizations. This project supports the need for data visualization experiences to be appealing, accommodate short and long-term exploration, and address a range of visitors’ prior knowledge. Front-end evaluation
African American and Latinx youth are often socialized towards athletic activity and sports participation, sometimes at the expense of their exploration of the range of potential career paths including those in the science, technology, engineering, and mathematics (STEM) fields. This project will immerse middle school youth in the rapidly growing world of sports data analytics and build their knowledge of statistics concepts and the data science process. The project will focus on the STEM interests and knowledge development of African American and Latinx youth, an underrepresented and underserved group in STEM. Researchers will explore the ways youths' social identities can and should serve as bridges towards future productive academic and professional identities including those associated with STEM learning and the STEM professions. The outcomes of the project will advance knowledge in promoting elements of informal learning experiences that build adolescents' motivation and persistence for productive participation in STEM courses and careers. This project is funded by the Advancing Informal STEM Learning program (AISL), which seeks to advance new approaches to and evidence-based understanding of the design and development of STEM learning opportunities for the public in informal environments, and the Innovative Technology Experiences for Students and Teachers program (ITEST), which funds projects that leverage innovative uses of technologies to prepare diverse youth for the STEM workforce, with a focus on broadening participation among underrepresented and underserved groups in STEM fields.
Over a three-year period, 250 middle school learners in the West Baltimore, Maryland and Hyattsville, Maryland areas will engage in three main learning activities: Summer Camp (three weeks), Sports Day Saturdays, and a Spring Summit. Through a partnership between the University of Maryland and Coppin State University, the project will utilize resources in multiple departments and units across both universities, and engage with youth sports leagues such as the American Athletic Union (AAU) to support participants' engagement in the data science process including collection of raw data, exploration of data, development of models, visualization, communication, and reporting of data, and data-driven decision making. Furthermore, youth participants will attend local AAU, college, and professional sporting events, and interact with members of coaching staffs to better understand the ways performance data technologies are utilized to inform recruitment and team performance. The mixed-methods research agenda for this project is guided by three main questions: (1) What elements of the project's model are most successful at supporting congruence of adolescents' academic identity, including STEM identity and social identity including athletic identity? (2) What elements support adolescents' motivation, and persistence for productive participation in current and future STEM courses? (3) To what extent did the project appear to influence participants' perceptions of their future professions? At multiple points throughout the experience, participants will complete surveys designed to document and assess statistics and data science knowledge; interest in STEM careers; academic, social and athletic identity development; and STEM course taking patterns. Researchers will also observe project activities, interview a focal group of participants, and survey participants' parents to identify elements of learning experiences that encourage and support adolescents' interest in STEM disciplines and STEM professions. The project team will develop conceptual and pedagogical frameworks that support middle school youth' engagement and interest in science, engineering, technology, and mathematics through repurposing spaces where these youths frequent. A major outcome of the project will be workforce preparation and offers a promising approach for encouraging youth to persist along STEM pathways, which may ultimately result in broadened participation in STEM workforces.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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.