Implementation of a permanent exhibition, on-line content, educational materials, and public programs exploring the history and cultural impact of video games.
Through the design, fabrication, and implementation of a 24,000-sq. ft. permanent, long-term gallery—tentatively entitled Digital Worlds—The Strong National Museum of Play will explore and share the history, influence, and experience of video games as they relate to culture, storytelling, human development, and the broader evolution of play. This gallery, the centerpiece of a transformational museum expansion, will include complementary and cohesive interactive exhibit spaces that showcase the history of video games through: (1) display of rare and unique historical artifacts; (2) use of multiple media formats that allow guests to discover the history of video games and their impact on society and culture; and (3) inclusion of one-of-a-kind interactive experiences that bring the history, art, and narrative structures of video games to life.
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
There is a dearth of prominent STEM role models for underrepresented populations. For example, according to a 2017 survey, only 3.1% of physicists in the United States are Black, only 2.1% are Hispanic, and only 0.5% are Native American. The project will help bridge these gaps by developing exhibits that include simulations of historical scientific experiments enacted by little-known scientists of color, virtual reality encounters that immerse participants in the scientists' discovery process, and other content that allows visitors to interact with the exhibits and explore the exhibits' themes. The project will develop transportable, interactive exhibits focusing on light: how we perceive light, sources of light from light bulbs to stars, uses of real and artificial light in human endeavors, and past and current STEM innovators whose work helps us understand, create, and harness light now. The exhibits will be developed in three stages, each exploring a characteristic of light (Color, Energy, or Time). Each theme will be explored via multiple deliveries: short documentary and animated films, virtual reality experiences, interactive "photobooths," and technology-based inquiry activities. The exhibit components will be copied at seven additional sites, which will host the exhibits for their audiences, and the project's digital assets will enable other STEM learning organizations to duplicate the exhibits. The exhibits will be designed to address common gaps in understanding, among adults as well as younger learners, about light. What light really is and does, in scientific terms, is one type of hidden story these exhibits will convey to general audiences. Two other types of science stories the exhibits will tell: how contemporary research related to light, particularly in astrophysics, is unveiling the hidden stories of our universe; and hidden stories of STEM innovators, past and present, women and men, from diverse backgrounds. These stories will provide needed role models for the adolescent learners, helping them learn complex STEM content while showing them how scientific research is conducted and the diverse community of people who can contribute to STEM innovations and discoveries.
The project deliverables will be designed to present complex physics content through coherent, immersive, and embodied learning experiences that have been demonstrated to promote engagement and deeper learning. The project will research whether participants, through interacting with these exhibits, can begin to integrate discrete ideas and make connections with complex scientific content that would be difficult without technology support. For example, students and other novices often lack the expertise necessary to make distinctions between what is needed and what is extra within scientific problems. The proposed study follows a Design-Based Research (DBR) approach characterized by iterative cycles of data collection, analysis, and reflection to inform the design of educational innovations and advance educational theory. Project research includes conceiving, building, and testing iterative phases, which will enable the project to capture the complexity of learning and engagement in informal learning settings. Research participants will complete a range of research activities, including focus group interviews, observation, and pre-post assessment of science content knowledge and dispositions.
By showcasing such role models and informing about related STEM content, this project will widen perspectives of audiences in informal learning settings, particularly adolescents from groups underrepresented in STEM fields. Research findings and methodologies will be shared widely in the informal STEM learning community, building the field's knowledge of effective ways to broaden participation in informal science learning, and thus increase broaden participation in and preparation for the STEM-based workforce.
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.
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TEAM MEMBERS:
Todd BoyetteJill HammJanice AndersonCrystal Harden
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.
Changes in household-level actions in the U.S. have the potential to reduce rates of greenhouse gas (GHG) emissions and climate change by reducing consumption of food, energy and water (FEW). This project will identify potential interventions for reducing household FEW consumption, test options in participating households in two communities, and collect data to develop new environmental impact models. It will also identify household consumption behavior and cost-effective interventions to reduce FEW resource use. Research insights can be applied to increase the well-being of individuals at the household level, improve FEW resource security, reduce climate-related risks, and increase economic competitiveness of the U.S. The project will recruit, train, and graduate more than 20 students and early-career scientists from underrepresented groups. Students will be eligible to participate in exchanges to conduct interdisciplinary research with collaborators in the Netherlands, a highly industrialized nation that uses 20% less energy and water per person than the U.S.
This study uses an interdisciplinary approach to investigate methods for reducing household FEW consumption and associated direct and indirect environmental impacts, including GHG emissions and water resources depletion. The approach includes: 1) interactive role-playing activities and qualitative interviews with homeowners; 2) a survey of households to examine existing attitudes and behaviors related to FEW consumption, as well as possible approaches and barriers to reduce consumption; and 3) experimental research in residential households in two case-study communities, selected to be representative of U.S. suburban households and appropriate for comparative experiments. These studies will iteratively examine approaches for reducing household FEW consumption, test possible intervention strategies, and provide data for developing systems models to quantify impacts of household FEW resource flows and emissions. A FEW consumption-based life cycle assessment (LCA) model will be developed to provide accurate information for household decision making and design of intervention strategies. The LCA model will include the first known farm-to-fork representation of household food consumption impacts, spatially explicit inventories of food waste and water withdrawals, and a model of multi-level price responsiveness in the electricity sector. By translating FEW consumption impacts, results will identify "hot spots" and cost-effective household interventions for reducing ecological footprints. Applying a set of climate and technology scenarios in the LCA model will provide additional insights on potential benefits of technology adoption for informing policymaking. The environmental impact models, household consumption tracking tool, and role-playing software developed in this research will be general purpose and publicly available at the end of the project to inform future education, research and outreach activities.
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TEAM MEMBERS:
David WatkinsBuyung AgusdinataChelsea SchellyRachael ShwomJenni-Louise Evans