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resource research Media and Technology
The cyberlearning community in the United States brings computer scientists and learning scientists together to design and study innovative learning technologies. The Cyberlearning Community Report: The State of Cyberlearning and the Future of Learning With Technology highlights examples of the exciting work our community is engaged in as we integrate the latest innovations in learning science and computer science into new research designs and methods. This work is also driving the need for new learning sciences in areas such as embodied cognition, identity, and affect, and requires advances
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TEAM MEMBERS: June Ahn Jodi Asbell-Clarke Matthew Berland Catherine Chase Noel Enyedy Judith Fusco Shuchi Grover Erica Halverson Kemi Jona H Chad Lane Wendy Martin Emma Mercier Tom Moher Amy Ogan Nichole Pinkard Joseph Polman Jeremy Roschelle Patricia Schank Katie Headrick Taylor Michelle Wilkerson Marcelo Worsley
resource project Media and Technology
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 Watkins Buyung Agusdinata Chelsea Schelly Rachael Shwom Jenni-Louise Evans