This project seeks to apply explainable artificial intelligence to the challenge of personalizing training for adult citizen scientists. The approach will be developed in the context of the Native Bee Watch (NBW) biodiversity monitoring project that began in 2016 at Colorado State University.
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TEAM MEMBERS:
Sarath SreedharanNikhil KrishnaswamyJill ZarestkyNathaniel Blanchard
Informal STEM learning experiences (ISLEs), such as participating in science, computing, and engineering clubs and camps, have been associated with the development of youth’s science, technology, engineering, and mathematics interests and career aspirations. However, research on ISLEs predominantly focuses on institutional settings such as museums and science centers, which are often discursively inaccessible to youth who identify with minoritized demographic groups. Using latent class analysis, we identify five general profiles (i.e., classes) of childhood participation in ISLEs from data
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TEAM MEMBERS:
Remy DouHeidi CianZahra HazariPhilip SadlerGerhard Sonnert
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
Refugee youth are particularly vulnerable to STEM disenfranchisement due to factors including limited or interrupted schooling following displacement; restricted exposure to STEM education; and linguistic, cultural, ethnic, socioeconomic, and racial minority status. Refugee youth may experience a gap in STEM skills and knowledge, and a conflict between the identities necessary for participation in their families and communities, and those expected for success in STEM settings. To conduct research to better understand these challenges, an interrelated set of activities will be developed. First, youth will learn principles of physics and computing by participating in cosmic ray research with physicists using an instructional approach that builds from their home languages and cultures. Then youth periodically share what they are learning in the cosmic ray research with their parents, siblings, and science teachers at family and community science events. Finally, youth conduct reflective research on their own STEM identity development over the course of the project. Research on learning will be conducted within and across these three strands to better understand how refugee youth develop STEM-positive identities. This project will benefit society by improving equity and diversity in STEM through (1) creating opportunities for refugee youth to participate in physics research and to develop computing skills and (2) producing knowledge on STEM identity development that may be applied more broadly to improve STEM education. Deliverables from this project include: (a) research publications on STEM identity and learning; (b) curriculum resources for teaching physics and computing to multilingual youth; (c) an online digital storytelling exhibit offering narratives about belonging in STEM research which can be shared with STEM stakeholders (policy makers, scientists, educators, etc.); and (d) an online database of cosmic ray data which will be available to physicists worldwide for research purposes. This Innovations in Development proposal 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.
This program is designed to provide multiple contexts, relationships, and modes across and within which the identity work of individual students can be studied to look for convergence or divergence. To achieve this goal, the research applies a linguistic anthropological framework embedding discourse analysis in a larger ethnography. Data collected in this study include field notes, audio and video recordings of naturalistic interactions in the cosmic ray research and other program activities, multimodal artifacts (e.g., students' digital stories), student work products, interviews, and surveys. Critically, this methodology combines the analysis of identity formation as it unfolds in moment-to-moment conversations (during STEM learning, and in conversations about STEM and STEM learning) with reflective tasks and the production of personal narratives (e.g., in digital stories and interviews). Documenting convergence and divergence of STEM identities across these sources of data offers both methodological and theoretical contributions to the field. The research will offer thick description of the discursive practices of refugee youth to reveal how they construct identities related to STEM and STEM disciplines across settings (e.g., during cosmic ray research, while creating digital stories), relationships (e.g., peer, parent, teacher), and the languages they speak (e.g., English, Swahili). The findings will be of potential value to instructional designers of informal learning experiences including those working with afterschool, museums, science centers and the like, educators, and scholars of learning and identity.
This Innovations in Development 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.
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TEAM MEMBERS:
Tino NyaweloJohn MatthewsJordan GertonSarah Braden
We developed a multi-touch interface for the citizen science video game Foldit, in which players manipulate 3D protein structures, and compared multi-touch and mouse interfaces in a 41-subject user study. We found that participants performed similarly in both interfaces and did not have an overall preference for either interface. However, results indicate that for tasks involving guided movement to dock protein parts, subjects using the multi-touch interface completed tasks more accurately with fewer moves, and reported higher attention and spatial presence. For tasks involving direct
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TEAM MEMBERS:
Thomas MuenderSadaab Ali GulaniLauren WestendorfClarissa VerishRainer MalakaOrit ShaerSeth Cooper
Although hundreds of citizen science applications exist, there is lack of detailed analysis of volunteers' needs and requirements, common usability mistakes and the kinds of user experiences that citizen science applications generate. Due to the limited number of studies that reflect on these issues, it is not always possible to develop interactions that are beneficial and enjoyable. In this paper we perform a systematic literature review to identify relevant articles which discuss user issues in environmental digital citizen science and we develop a set of design guidelines, which we evaluate
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TEAM MEMBERS:
Artemis SkarlatidouAlexandra HamiltonMichalis VitosMuki Haklay
This INSPIRE award is partially funded by the Cyber-Human Systems Program in the Division of Information and Intelligent Systems in the Directorate for Computer Science and Engineering, the Gravitational Physics Program in the Division of Physics in the Directorate for Mathematical and Physical Sciences, and the Office of Integrative Activities.
This innovative project will develop a citizen science system to support the Advanced Laser Interferometer Gravitational wave Observatory (aLIGO), the most complicated experiment ever undertaken in gravitational physics. Before the end of this decade it will open up the window of gravitational wave observations on the Universe. However, the high detector sensitivity needed for astrophysical discoveries makes aLIGO very susceptible to noncosmic artifacts and noise that must be identified and separated from cosmic signals. Teaching computers to identify and morphologically classify these artifacts in detector data is exceedingly difficult. Human eyesight is a proven tool for classification, but the aLIGO data streams from approximately 30,000 sensors and monitors easily overwhelm a single human. This research will address these problems by coupling human classification with a machine learning model that learns from the citizen scientists and also guides how information is provided to participants. A novel feature of this system will be its reliance on volunteers to discover new glitch classes, not just use existing ones. The project includes research on the human-centered computing aspects of this sociocomputational system, and thus can inspire future citizen science projects that do not merely exploit the labor of volunteers but engage them as partners in scientific discovery. Therefore, the project will have substantial educational benefits for the volunteers, who will gain a good understanding on how science works, and will be a part of the excitement of opening up a new window on the universe.
This is an innovative, interdisciplinary collaboration between the existing LIGO, at the time it is being technically enhanced, and Zooniverse, which has fielded a workable crowdsourcing model, currently involving over a million people on 30 projects. The work will help aLIGO to quickly identify noise and artifacts in the science data stream, separating out legitimate astrophysical events, and allowing those events to be distributed to other observatories for more detailed source identification and study. This project will also build and evaluate an interface between machine learning and human learning that will itself be an advance on current methods. It can be depicted as a loop: (1) By sifting through enormous amounts of aLIGO data, the citizen scientists will produce a robust "gold standard" glitch dataset that can be used to seed and train machine learning algorithms that will aid in the identification task. (2) The machine learning protocols that select and classify glitch events will be developed to maximize the potential of the citizen scientists by organizing and passing the data to them in more effective ways. The project will experiment with the task design and workflow organization (leveraging previous Zooniverse experience) to build a system that takes advantage of the distinctive strengths of the machines (ability to process large amounts of data systematically) and the humans (ability to identify patterns and spot discrepancies), and then using the model to enable high quality aLIGO detector characterization and gravitational wave searches
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TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
As part of its overall strategy to enhance learning in informal environments, the Advancing Informal STEM Learning (AISL) program funds innovative research, approaches and resources for use in a variety of settings. In this exploratory Change Makers project, the Concord Consortium will develop, test, and evaluate a citizen science program that leverages innovative technology, such that youth engage directly with energy issues through scientific inquiry. The project will create the Infrared Street View, a citizen science program that aims to produce a thermal version of Google's Street View using an affordable infrared camera attached to a smartphone. The infrared camera serves as a high-throughput data acquisition instrument that collects thousands of temperature data points each time a picture is taken. Youth will collect massive geotagged thermal data that have considerable scientific and educational value for visualizing energy usage and improving energy efficiency at all levels. The Infrared Street View program will provide a Web-based platform for youth and anyone interested in energy efficiency to view and analyze the aggregated data to identify possible energy losses. By sharing their scientific findings with stakeholders, youth will make changes to the way energy is being used. The project will start with school, public, and commercial buildings in selected areas where performing thermal scan of the buildings and publishing their thermal images for educational and research purposes are permitted by school leaders, town officials, and property owners. In collaboration with high schools and out-of-school programs in Massachusetts, this project will conduct pilot-tests with approximately 200 students.
To contribute to advancing learning, the study will probe three research questions: 1) Under what circumstances can technology bridge out-of-school and classroom science learning and improve learning on both sides? 2) To what extent can unobtrusive assessment based on data mining support research and evaluation of student learning in out-of-school settings? and 3) To what extent can instructional intelligence built into the app used in the program help students learn in out-of-school programs and improve the quality of data they contribute to the citizen science project? Data sources for investigating these questions include students' interaction data with the app logged behind the scenes and the images they have taken, as well as results based on traditional assessments from a small number of participants. Throughout the project, staff will widely disseminate project products and findings through the Internet, science fairs, conferences, publications, and partner networks. An eight-member Advisory Board consisting of cleantech experts, science educators, and educational researchers will oversee and evaluate this project.
This article examines certain guiding tenets of science journalism in the era of big data by focusing on its engagement with citizen science. Having placed citizen science in historical context, it highlights early interventions intended to help establish the basis for an alternative epistemological ethos recognising the scientist as citizen and the citizen as scientist. Next, the article assesses further implications for science journalism by examining the challenges posed by big data in the realm of citizen science. Pertinent issues include potential risks associated with data quality
Computational social science represents an interdisciplinary approach to the study of reality based on advanced computer tools. From economics to political science, from journalism to sociology, digital approaches and techniques for the analysis and management of large quantities of data have now been adopted in several disciplines. The papers in this JCOM commentary focus on the use of such approaches and techniques in the research on science communication. As the papers point out, the most significant advantages of a computational approach in this sector include the chance to open up a range
Lack of diversity in science and engineering education has contributed to significant inequality in a workforce that is responsible for addressing today's grand challenges. Broadening participation in these fields will promote the progress of science and advance national health, prosperity and welfare, as well as secure the national defense; however, students from underrepresented groups, including women, report different experiences than the majority of students, even within the same fields. These distinctions are not caused by the students' ability, but rather by insufficient aspiration, confidence, mentorship, instructional methods, and connection and relevance to their cultural identity. The long-term vision of this project is to amplify the impact of a successful broadening participation model at the University of Maine, the Stormwater Research Management Team (SMART). This program trains students and mentors in using science and engineering skills and technology to research water quality in their local watershed. Students engage in numerous science and technology fields: engineering design, data acquisition, analysis and visualization, chemistry, environmental science, biology, and information technology. Students also connect with a diversity of professionals in water and engineering in government, private firms and non-profits. SMART has augmented the traditional science and engineering classroom by engaging students in guided mentored apprenticeships that address community problems.
Technical
This pilot project will form a collaborative and define a strategic plan for scale-up to a national alliance to increase the long-term success rate of underrepresented minority students in science, engineering, and related fields. The collaborative of multiple and varied organizations will align to collectively contribute time and resources to a pre-college educational pathway. There are countless isolated programs that offer short-term interventions for underrepresented and minority students; however, there is lack of organizational coordination for aligning current program offerings, sharing best practices, research results or program outcomes along the education to workforce pathway. The collaborative activities will focus on the transition grades (e.g., 4-5, 8, and high school) and emphasize relationships among skills, confidence, culture and future careers. Collaborative partners will establish a centralized infrastructure in each location to coordinate recruiting of invested community leaders, educators, and parents, around a common agenda by designing, deploying and continually assessing a stormwater-themed project that addresses their location and demographic specific needs. This collaborative community will consist of higher education faculty and students, K-12 students, their caregivers, mentors, educators, stormwater districts, state and national environmental protection agencies, departments of education, and other for-profit and non-profit organizations. The collaborative will address the need for research on mechanisms for change, collaboration, and negotiation regarding the greater participation of under-represented groups in the science and technology workforce.
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TEAM MEMBERS:
Mohamed MusaviVenkat BhethanabotlaCary JamesVemitra WhiteLola Brown