While previous studies have found games and gaming to be a new and innovative communication strategy to inform the public and citizens about scientific research and engage them with it, this article addresses the under-researched question of credibility aspects in research-based gaming. The study analyses agricultural stakeholders' discussions on the credibility of scientific descriptions in The Maladaptation Game — a game based on research on climate change maladaptation in Nordic agriculture. The analysis of focus group transcripts and frame credibility finds that players attribute
This NOVA multiplatform media initiative consisted of a 2-hour nationally broadcast PBS documentary, Polar Extremes; a 10-part original digital series, Antarctic Extremes; an interactive game, Polar Lab; accompanying polar-themed digital shorts, radio stories, text reporting, and social media content; a collection of educational resources on PBS LearningMedia; and community screening events and virtual field trips for science classrooms. Across multiple media platforms the project’s video content had nearly 13 million views.
The research explored the potential for informal STEM learning
Becoming computationally literate is increasingly crucial to everyday life and to expanding workforce capacity. Research suggests that computational literacy--knowing what, when, how, and why to use the ideas of computer science, in combination with the capacity to view problems and potential solutions through the lens of computational structures and procedures--can be supported through digital game play. This project aims to develop a social and creative exhibit game that foregrounds aspects of computer science, specifically artificial intelligence (AI) and computer programming, in ways that enable youth to explore, construct, and share computational complex systems content with one another and other museum visitors. To play the game, pairs of youth visitors will use code cards to program the behavior of AI animals in a virtual forest. As they do so, youth will engage with computational literacy practices, such as basic computer programming, describing their computational ideas, and doing computational problem solving with their friends. Their activity will be projected on a large screen as a strategy for enabling youth to test, rehearse, and communicate their computational ideas and to also interest other visitors into computational problem solving.
Using multi-perspective and iterative design-based research, university learning scientists, museum practitioners, and game developers will pursue research questions around how science museums can better engage youth who are traditionally underrepresented in computer science in complex computational practices. Data sources will include interactive-log data, observations of visitor interactions with the game, visitor interviews, and visitor surveys. A multimodal and mixed methods approach that searches for convergences between qualitative analysis, quantitative analysis, and learning analytics will be used to generate research findings. Changes in computational literacy will be assessed by evaluating what problems visitors choose to solve with programming, how they frame those problems, and their selections from among possible solutions, what they program, how they program, and how they describe programming ideas. The results of this project will include: 1) a social, interactive gameplay experience that supports the development of computational literacy; 2) design principles for game-based exhibits that facilitate development of computational literacy; and 3) new knowledge of variations in design and gameplay across diverse gameplay users, including those from underrepresented groups in computer science. It is anticipated that 1,000 museum youth visitors will directly participate in the study.
This 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.
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TEAM MEMBERS:
Matthew BerlandLeilah LyonsMatthew Cannady
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