This assessment serves as the summative assessment of the IMLS-funded project at KU Biodiversity Institute and Natural History Museum: Natural History Mystery: Immersing families in a problem-solving game using museum collections. The assessment employs a mixed methods approach, in which both quantitative and qualitative data are collected. More specifically, quantitative data are generated from surveys that are administered to participants at the beginning and end of the game and analyzed by using descriptive statistics (i.e., mean, standard deviation, and histogram) and paired sample t-test
The University of Kansas Natural History Museum, in collaboration with the University of California Museum of Paleontology, will develop, test, and deploy an immersive educational game on the topic of evolution and common ancestry. The museum will frame the game with a narrative that involves tracing the origin of a zoonotic disease (infectious disease that is transmitted between species from animals to humans or from humans to animals). Played on the museum floor, the escape room-inspired game will explore innovative formats for museum learning and engagement. It is being designed for families with children ages 7 to 12, and by visiting groups of schoolchildren in grades 3 to 5.
From a strategic communication perspective, for any communication to be effective, it must be audience-centered, with content and delivery channels that are relevant to its intended target. When trying to reach culturally specific communities or other groups that are not otherwise connected with science research, it is crucial to partner with community members to co-create content through media that is appealing and culturally competent. This commentary considers some examples including storytelling through ‘fotonovelas’ and radio stories, community drama and serious games.
A report following the 2016 Environmental Health Summit recommended engaging citizens in creating their own knowledge and solutions, thus ensuring that their concerns are adequately addressed and promoting sustainability of community projects. Indeed, citizen science has the potential to initiate a cascade of events with a positive ripple effect that includes a more diverse future STEM and biomedical workforce. This SEPA proposal involves the establishment of WE ENGAGE – an informal, citizen science-based, environmental health experiential learning program designed in partnership with and for under resourced communities struggling with health and environmental health challenges. Its purpose is to actively engage and build the citizen science capacity of citizens living in a single cluster of three contiguous under resourced, minority Cincinnati neighborhoods where generational challenges continue to plague residents despite the presence of established academic-community partnerships. Our hypothesis is that community-informed, experiential learning opportunities outside of the classroom that are structured, multi-generational, and story-based will encourage a) the active asking, discussion about, and answering of relevant complex health and environmental questions so that individuals and communities can plan action steps to make better health choices and pursue healthier environments, and b) greater interest and confidence in pursuing formal biomedical/STEM education and STEM careers. Our program has three specific aims: 1) We will co-create tailored story- based (graphic novel style) STEM education materials with a community advisory board and offer informal STEM education and research training to our target communities; 2) we will facilitate the application of scientific inquiry skills to improve health via community-led health fairs that use an innovative electronic health passport platform to collect data and through facilitated community discussions of health fair data to generate motivating stories to share; and 3) we will facilitate the application of scientific inquiry skills to foster community pride and activism in promoting healthier/safer built environments via walking environmental assessments. As in aim 2, facilitated discussions will be held to spur future community based participatory research studies and interventions. Critical to our success is the concept of storytelling. Storytelling is a foundation of the human experience. A key purpose of storytelling is not just understanding the world, but positively transforming it. It is a common language. Bringing together STEM concepts in the form of a story increases their appeal and meaning. Later, the very process of community data collection gives individuals a voice. In a data story, hundreds to millions of voices can be distilled into a single narrative that can help community members probe important underlying associations and get to the root causes of complicated health issues relevant to their communities. Through place based, understandable, motivating data stories, the community’s collective voice is clearer—leading to relevant and viable actions that can be decided and taken together. From preventing chronic disease, to nurturing healthier environments, to encouraging STEM education — stories have unlimited potential.
Public Health Relevance Statement:
Narrative WE ENGAGE is an informal citizen science-based, experiential learning program designed in partnership with and for middle schoolers to adults living in under resourced minority communities. Using the power of data collection and storytelling, its purpose is to actively engage citizens in STEM/research education and training to encourage a more diverse future workforce and to sustainably build local capacity to ask and answer complex health and environmental questions relevant to their communities. Further, by engaging citizens and giving them a more equitable stake in the research process, they are better able to discover their own solutions.
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TEAM MEMBERS:
Melinda Sue ButschkovacicSusan Ann Hershberger
A team of experts from five institutions (University of Minnesota, Adler Planetarium, University of Wyoming, Colorado State University, and UC San Diego) links field-based and online analysis capabilities to support citizen science, focusing on three research areas (cell biology, ecology, and astronomy). The project builds on Zooniverse and CitSci.org, leverages the NSF Science Gateways Community Institute, and enhances the quality of citizen science and the experience of its participants.
This project creates an integrated Citizen Science Cyberinfrastructure (CSCI) framework that expands the capacity of research communities across several disciplines to use citizen science as a suitable and sustainable research methodology. CSCI produces three improvements to the infrastructure for citizen science already provided by Zooniverse and CitSci.org:
Combining Modes - connecting the process of data collection and analysis;
Smart Assignment - improving the assignment of tasks during analysis; and
New Data Models - exploring the Data-as-Subject model. By treating time series data as data, this model removes the need to create images for classification and facilitates more complex workflows. These improvements are motivated and investigated through three distinct scientific cases:
Biomedicine (3D Morphology of Cell Nucleus). Currently, Zooniverse 'Etch-a-Cell' volunteers provide annotations of cellular components in images from high-resolution microscopy, where a single cell provides a stack containing thousands of sliced images. The Smart Task Assignment capability incorporates this information, so volunteers are not shown each image in a stack where machines or other volunteers have already evaluated some subset of data.
Ecology (Identifying Individual Animals). When monitoring wide-ranging wildlife populations, identification of individual animals is needed for robust estimates of population sizes and trends. This use case combines field collection and data analysis with deep learning to improve results.
Astronomy (Characterizing Lightcurves). Astronomical time series data reveal a variety of behaviors, such as stellar flares or planetary transits. The existing Zooniverse data model requires classification of individual images before aggregation of results and transformation back to refer to the original data. By using the Data-as-Subject model and the Smart Task Assignment capability, volunteers will be able to scan through the entire time series in a machine-aided manner to determine specific light curve characteristics.
The team explores the use of recurrent neural networks (RNNs) to determine automated learning architectures best suited to the projects. Of particular interest is how the degree to which neighboring subjects are coupled affects performance. The integration of existing tools, which is based on application programming interfaces (APIs), also facilitates further tool integration. The effort creates a citizen science framework that directly advances knowledge for three science use cases in biomedicine, ecology, and astronomy, and combines field-collected data with data analysis. This has the ability to solve key problems in the individual applications, as well as benefiting the research of the dozens of projects on the Zooniverse platform. It provides benefits to researchers using citizen scientists, and to the nearly 1.6 million citizen scientists themselves.
This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Research on Learning in Formal and Informal Settings, within the NSF Directorate for Education and Human Resources.
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.
As part of its overall effort to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program, seeks to advance new approaches to, and evidence-based understanding of, the design and development of science, technology, engineering, and mathematics (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. In alignment with these aims, the STEM + Digital Literacies (STEM+L) project will investigate science fiction as an effective mechanism to attract and immerse adolescents (ages 10-13) from diverse cultural backgrounds in environmental and human health content and socio-scientific issues. This work is particularly novel, as the current knowledge base is limited, and largely addresses the high school level. Therefore, the results of the proposed effort could yield important findings regarding the feasibility of this activity as an effective platform for science learning and engagement for younger students. As such, STEM+L would not only advance knowledge in the field but would also contribute to a growing AISL portfolio on digital literacy and learning.
STEM+L is an early stage Innovations in Development project that will engage thirty middle school students in out of school time experiences. Over a twenty-four-week period, students will work collaboratively in groups in-person and online with their peers and field experts to design, develop, and produce STEM content rich, multimedia science fictions. The in-person learning experiences will take place on the University of Miami campus during the summer and academic year. Culminating activities include student presentations online and at a local Science Fiction Festival. The research component will employ an iterative, design-based approach. Four research questions will be explored: (a) How do students learn science concepts and multimodal digital literacies through participating in the STEM+L Academy? (b) How do students change their views in STEM related subject matter and in pursuing STEM related careers? (c) How do students participate in the STEM+L Academy? (d) How do we best support students' participation and learning of STEM+L in face-to-face and online environments? Data collection methods include video records, student-generated artifacts, online surveys, embedded assessments, interviews, and multimodal reflections. Comparative case analysis and a mixed methods approach will be employed. A rigorous evaluation will be conducted by a critical external review board. Inclusive and innovative dissemination strategies will ensure that the results of the research and program reach a broad range of audiences including both informal and formal STEM and literacy educators and researchers, learning scientists, local communities, and policy makers through national and international conference presentations, journal publications, Web2.0 resources, and community outreach activities.
As part of the National Science Foundation (NSF) funding for the In Defense of Food project directed by Kikim Media, the independent evaluation firm Knight Williams Inc.1 conducted a summative evaluation of the project’s key deliverables, which included: a PBS television broadcast program, an outreach effort, and an educational curriculum. This report (Study 3 of 3) considers the In Defense of Food curriculum and, in particular, educators’ reactions to the curriculum in terms of perceived appeal, ease of implementation, and learning value. Feedback was gathered from educators who were surveyed
As part of the National Science Foundation (NSF) funding for the In Defense of Food project directed by Kikim Media, the independent evaluation firm Knight Williams Inc.1 conducted a summative evaluation of the project’s key deliverables, which included: a PBS television broadcast program, an outreach effort, and an educational curriculum. This report (Study 2 of 3) focuses on the outreach effort.
As part of the National Science Foundation (NSF) funding for the In Defense of Food project directed by Kikim Media, the independent evaluation firm Knight Williams Inc. conducted a summative evaluation of the project’s key deliverables, which included: a PBS television broadcast program, an outreach effort, and an educational curriculum. This report (Study 1 of 3) considers the film’s overall appeal, clarity, learning value, and motivational impact among viewers matching the film’s target audience, and focuses on the following six questions:
1) Did viewers find the film appealing, engaging
Of all the online information tools that the public relies on to collect information and share opinions about scientific and environmental issues, Twitter presents a unique venue to assess the spontaneous and genuine opinions of networked publics, including those about a focusing event like the Fukushima Daiichi nuclear accident following the 2011 Tohoku earthquake and tsunami. Using computational linguistic algorithms, this study analyzes a census of English-language tweets about nuclear power before, during, and after the Fukushima nuclear accident. Results show that although discourse about
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TEAM MEMBERS:
Nan LiHeather AkinLeona Yi-Fan SuDominique BrossardMichael XenosDietram Scheufele
The management of health risks related to scientific and technological innovations has been the focus of a heated debate for a few years now. In some cases, like the campaigns against the use of GMOs in agriculture, this debate has degenerated into a political and social dispute. Even risk analysis studies, which appeared in the 1970s in the fields of nuclear physics and engineering and were later developed by social sciences as well, have given completely different, and at times contradictory, interpretations that, in turn, have given rise to bitter controversies.
Antibiotic resistance is an increasing global threat involving many actors, including the general public. We present findings from a content analysis of the coverage of antibiotic resistance in the Swedish print media with respect to the risk communication factors cause, magnitude and countermeasures. The most commonly reported cause of development and spread of resistance was unnecessary prescription of antibiotics. Risk magnitudes were mostly reported qualitatively rather than using quantitative figures. Risk-reduction measures were analyzed using a framework that distinguishes between