We present the assets that collaboration across a land grant university brought to the table, and the Winterberry Citizen Science program design elements we have developed to engage our 1080+ volunteer berry citizen scientists ages three through elder across urban and rural, Indigenous and non-Indigenous, and formal and informal learning settings.
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TEAM MEMBERS:
Katie SpellmanJasmine ShawChristine VillanoChrista MulderElena SparrowDouglas Cost
We used a youth focused wild berry monitoring program that spanned urban and rural Alaska to test this method across diverse age levels and learning settings.
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TEAM MEMBERS:
Katie SpellmanDouglas CostChristine Villano
In this study, we examined how two different CCS models, a contributory design and a co-created design, influenced science self-efficacy and science interest among youth CCS participants.
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TEAM MEMBERS:
Sarah ClementKatie SpellmanLaura OxtobyKelly KealyKarin BodonyElena SparrowChristopher Arp
The Arctic Harvest-Public Participation in Scientific Research (which encompasses the Winterberry Citizen Science program), a four-year citizen science project looking at the effect of climate change on berry availability to consumers has made measurable progress advancing our understanding of key performance indicators of highly effective citizen science programs.
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TEAM MEMBERS:
Angela LarsonKelly KealyMakaela Dickerson
This project builds off prior work conducted for the Science Center Public Forums project (NA15SEC008005) where eight forums were held at different sites across the US related to four climate hazards (drought, sea level rise, extreme heat, and extreme precipitation).
The project team published a research synopsis article with Futurum Science Careers in Feb 2023 called “How Can Place Attachment Improve Scientific Literacy?”
An adapted three-dimensional model of place attachment is proposed as a theoretical framework from which place-based citizen science experiences and outcomes might be empirically examined in depth.
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TEAM MEMBERS:
Julia ParrishYurong HeBenjamin Haywood
PocketMacro is a mobile app designed by the Learning Media Design Center at Carnegie Mellon University in collaboration with Stroud Water Research Center, Carnegie Museum of Natural History and Clemson University, and stakeholder input. The PocketMacro app aims to help users better identify benthic macroinvertebrates commonly found in streams and other waterways. Rockman et al Cooperative (REA), an independent educational evaluation group, designed a summative study to explore the effectiveness of the app in supporting users’ aquatic macroinvertebrate identification. The purpose of the
This project is designed to support collaboration between informal STEM learning (ISL) researchers, designers, and educators with sound researchers and acoustic ecologists to jointly explore the role of auditory experiences—soundscapes—on learning. In informal STEM learning spaces, where conversation advances STEM learning and is a vital part of the experience of exploring STEM phenomena with family and friends, attention to the impacts of soundscapes can have an important bearing on learning. Understanding how soundscapes may facilitate, spark, distract from, or even overwhelm thinking and conversation will provide ISL educators and designers evidence to inform their practice. The project is structured to reflect the complexity of ISL audiences and experiences; thus, partners include the North Park Village Nature Center located in in a diverse immigrant neighborhood in Chicago; Wild Indigo, a Great Lakes Audubon program primarily serving African American visitors in Midwest cities; an after-school/summer camp provider, STEAMing Ahead New Mexico, serving families in the rural southwest corner of New Mexico, and four sites in Ohio, MetroParks, Columbus Zoo and Aquarium, Franklin Park Conservatory and Botanical Gardens, and the Center of Science and Industry.
Investigators will conduct large-scale exploratory research to answer an understudied research question: How do environmental sounds impact STEM learning in informal learning spaces? Researchers and practitioners will characterize and describe the soundscapes throughout the different outdoor and indoor exhibit/learning spaces. Researchers will observe 800 visitors, tracking attraction, attention, dwell time, and shared learning. In addition to observations, researchers will join another 150 visitors for think-aloud interviews, where researchers will walk alongside visitors and capture pertinent notes while visitors describe their experience in real time. Correlational and cluster analyses using machine learning algorithms will be used to identify patterns across different sounds, soundscapes, responses, and reflections of research participants. In particular, the analyses will identify characteristics of sounds that correlate with increased attention and shared learning. Throughout the project, a team of evaluators will monitor progress and support continuous improvement, including guidance for developing culturally responsive research metrics co-defined with project partners. Evaluators will also document the extent to which the project impacts capacity building, and influences planning and design considerations for project partners. This exploratory study is the initial in a larger research agenda, laying the groundwork for future experimental study designs that test causal claims about the relationships between specific soundscapes and visitor learning. Results of this study will be disseminated widely to informal learning researchers and practitioners through workshops, presentations, journal articles, facilitated conversations, and a short film that aligns with the focus and findings of the research.
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TEAM MEMBERS:
Martha MersonJustin MeyerDaniel Shanahan
Mathematizing, Visualizing, and Power (MVP): Appalachian Youth Becoming Data Artists for Community Learning is a three-year Advancing Informal STEM Learning, Innovations and Development, project that focuses on community-centered data exploration catalyzed by youth. The project develops statistical artistry among young people in East Tennessee Appalachian communities and enables these youth to share their data visualizations with their communities to foster collective reflection and understanding. The creative work generated by the MVP project will be compelling in two ways, both as statistical art and as powerful statements giving voice to the experience of communities. Critical aspects of the MVP model include (1) youth learning sessions that position youth as owners of data and producers of knowledge and (2) Community Learning Events that support community learning as youth learning occurs. The MVP project has a primary focus on broadening the STEM participation of underrepresented communities of Appalachia. The project’s mission is to increase the learning and life outcomes of young people and communities of Appalachia by creating a meaningful foundation of data science and collective data exploration. The University of Tennessee partners with Pellissippi State Community College, Drexel University, and the Boys & Girls Club of the Tennessee Valley to bring together a convergent team of community members, practitioners, and professionals, with the expertise to carry out the project. The project will impact approximately 120 youth and 3800 of their East Tennessee community members. The research generated will inform how to engage community members in learning about community issues through the exploration of datasets relevant to participants.
The field of STEM education is in urgent need of knowledge about effective models to inspire community-based data exploration with young people as leaders in these efforts. The MVP project includes engaging youth with meaningful problems, building a discourse community with possibilities for action, re-positioning youth as knowledge producers within their own communities, leveraging linguistic and cultural resources of the youth participants and their communities, and implementing critical events that support substantial interaction between youth, community members, and the data visualizations. MVP builds on the idea that the design of data visualizations requires an understanding of both data science and artistic design. Research will inform the model of community engagement, examine data artists’ identities, and document community learning. The MVP model will be designed, developed, tested, and refined through three cycles of design-based research. The overarching research question guiding these cycles is: What affordances (and delimitations) related to identity and learning does the model provide for MVP Youth and community members? Data sources for the project include: fieldnotes, portfolios created by MVP Youth, youth pre/post interviews, observations of the learning sessions, a project documentary, surveys for youth and community members, interviews with community members, and audience feedback. The National Institute for STEM Evaluation and Research (NISER) will provide formative and summative evaluation about project activities. Formative feedback will be integrated into the ongoing research cycles. The research conducted will inform (1) the community learning model; (2) the integrated pedagogy and curriculum of the MVP Youth learning sessions that emphasize data science through design arts; and, (3) research on community learning and youth identity. Findings will be shared through conferences, academic and practitioner-focused journals, a video documentary, a Summit on Engaging Youth and Communities in Data, and a project website.
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TEAM MEMBERS:
Lynn HodgeElizabeth DyerJoy BertlingCarlye Clark
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Project Harvest is a co-created citizen science project that investigates the quality of household environments in Arizona communities neighboring active or legacy mining and/or toxic release. Project Harvest is a response to the community-driven questions, “Are there pollutants in harvested rainwater? Can I use the harvested rainwater for my garden?"