Over a three-year period, the Lawrence Hall of Science will conduct research on the conversations of groups and families encountering an Augmented Reality (AR) experience in a museum environment. The research program will identify which design elements best facilitate conversations among groups of visitors, and determine if these conversations are both rich in scientific content and gender-balanced. The project will focus on four specific activities: understanding the learning associated with current AR activities, implementing design-based research to develop visitor conversation supports, designing and developing new AR programs with embedded conversation supports, and conducting iterative hypothesis-based research on how learning conversations happen in AR learning environments. The museum community will gain insights on design principles for supporting collaborative learning using AR. Project staff will disseminate results via conference workshops for museum professionals on designing AR to enhance family learning, and through publication in professional journals.
Hero Elementary is a transmedia educational initiative aimed at improving the school readiness and academic achievement in science and literacy of children grades K-2. With an emphasis on Latinx communities, English Language Learners, youth with disabilities, and children from low-income households, Hero Elementary celebrates kids and encourages them to make a difference in their own backyards and beyond by actively doing science and using their Superpowers of Science. The project embeds the expectations of K–2nd NGSS and CCSS-ELA standards into a series of activities, including interactive games, educational apps, non-fiction e-books, hands-on activities, and a digital science notebook. The activities are organized into playlists for educators and students to use in afterschool programs. Each playlist centers on a meaningful conceptual theme in K-2 science learning.
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TEAM MEMBERS:
Joan FreeseMomoko HayakawaBryce Becker
This Smart and Connected Community (SCC) project will partner with two rural communities to develop STEMports, an innovative Science, Technology, Engineering and Mathematics (STEM) learning game for workforce development. The game's activities will take players on localized Augmented Reality (AR) missions to both engage in STEM learning challenges and discover emerging STEM careers in their community, specifically highlighting innovations in the fields of sustainable agriculture and aquaculture, forest products, and renewable energy. Community Advisory Teams (CATs) and co-design teams, including youth, representatives from the targeted emerging STEM economies, and decision-makers will partner with project staff to co-design STEMports that reflect the interests, cultural contexts, and envisioned STEM industries of the future for each community.
The project will: (a) design and pilot an AR game for community STEM workforce development; (b) develop and adapt a community engagement process that optimizes community networking for co-designing the gaming application and online community; and (c) advance a scalable process for wider applications of STEMports. This project is a collaboration between the Maine Mathematics and Science Alliance and the Field Day Lab at the University of Wisconsin-Madison to both build and research the co-designing of a SCC based within an AR environment. The project will contribute knowledge to the informal STEM learning, community development, and education technology fields in four major ways:
Deepening the understanding of how innovative technological tools support rural community STEM knowledge building as well as STEM identity and workforce interest.
Identifying design principles for co-designing the STEMports community related to the technological design process.
Developing social network approaches and analytics to better understand the social dimensions and community connections fostered by the STEMport community.
Understanding how participants' online and offline interactions with individuals and experiences builds networks and knowledge within a SCC.
With the scaling of use by an ever-growing community of players, STEMports will provide a new AR-based genre of public participation in STEM and collective decision making. The research findings will add to the emerging literature on community-wide education, innovative education technologies, informal STEM learning (especially place-based learning and STEM ecosystems), and participatory design research.
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.
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.
In this project, education researchers, environmental scientists, and educators will develop a computer tool to let STEM educators and curriculum developers build local environmental science models. The system will use data about land use to automatically construct map-based simulations of any area in the United States. Users will be able to choose from a range of environmental and economic issues to include in these models. The system will create simulations that ask students to change to patterns of land use -- for example, increasing land zoned for housing, or open land, or industrial development -- to try to meet environmental and social goals. As a result, students will be able to learn about the interaction of environmental and economic issues relevant to their own city, town, neighborhood, or region. These map-based simulations will be incorporated into an existing science, technology, engineering, and mathematics (STEM) education tool, Land Science, in which learners work in a fictional planning office to study how zoning affects economic and environmental issues in a community. Research has shown that Land Science is mode effective when learners are exploring issues in an area near their home, and the current study will investigate how and why local simulations improve environmental science learning. This project is funded by the Advancing Informal STEM Learning (AISL) program which supports work to enhance learning in informal environments by funding innovative research, approaches, and resources for use in a variety of settings.
In this project, the research team will build, test, and deploy a toolkit that will allow informal STEM educators and developers of informal STEM programming to easily adapt an existing environmental science learning environment, which consists of a place-based virtual internship in urban planning and ecology, to their local contexts, learning objectives, and learner populations. Land Science is a virtual internship in which young people explore the environmental and socio-economic impacts of land-use decisions. To do so, they play the role of interns at an urban planning firm developing a new land-use proposal for the city of Lowell, Massachusetts: they read reports, virtually visit sites, determine stakeholder priorities, and use a geographic information system (GIS) model to evaluate the socio-economic and environmental impacts of land-use choices. No one plan can satisfy all stakeholders, so learners must compromise to create an effective plan and justify their decisions. Land Science has been shown to improve civic engagement, interest in eco-social issues, and understanding of scientific models, but it is most effective when the location of the virtual internship is in or near the learners' home town. To improve the accessibility and impact of this effective learning intervention, the interdisciplinary research team, which includes learning scientists, land-use experts, and informal STEM educators, will develop a Local Environmental Modeling toolkit, which will allow educators to change the location of the simulation and the stakeholder groups, zoning codes, and environmental and socio-economic indicators included in the land-use model. The system will ensure that the model produced is functional, realistic, and appropriately complex. The localized versions of Land Science produced by informal STEM educators will be used in a range of contexts and locations, allowing the research team to study the effects of an online, place-based learning intervention on environmental science learning, STEM interest and motivation, and civic engagement.
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TEAM MEMBERS:
David ShafferKristen ScopinichHolly GibbsJeffrey Linderoth
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