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 project investigates long-term human-robot interaction outside of controlled laboratory settings to better understand how the introduction of robots and the development of socially-aware behaviors work to transform the spaces of everyday life, including how spaces are planned and managed, used, and experienced. Focusing on tour-guiding robots in two museums, the research will produce nuanced insights into the challenges and opportunities that arise as social robots are integrated into new spaces to better inform future design, planning, and decision-making. It brings together researchers from human geography, robotics, and art to think beyond disciplinary boundaries about the possible futures of human-robot co-existence, sociality, and collaboration. Broader impacts of the project will include increased accessibility and engagement at two partner museums, interdisciplinary research opportunities for both undergraduate and graduate students, a short video series about the current state of robotic technology to be offered as a free educational resource, and public art exhibitions reflecting on human-robot interactions. This project will be of interest to scholars of Science and Technology Studies, Human Robotics Interaction (HRI), and human geography as well as museum administrators, educators and the general public.
This interdisciplinary project brings together Science and Technology Studies, Human Robotics Interaction (HRI), and human geography to explore the production of social space through emerging forms of HRI. The project broadly asks: How does the deployment of social robots influence the production of social space—including the functions, meanings, practices, and experiences of particular spaces? The project is based on long-term ethnographic observation of the development and deployment of tour-guiding robots in an art museum and an earth science museum. A social roboticist will develop a socially-aware navigation system to add nuance to the robots’ socio-spatial behavior. A digital artist will produce digital representations of the interactions that take place in the museum, using the robot’s own sensor data and other forms of motion capture. A human geographer will conduct interviews with museum visitors and staff as well as ethnographic observation of the tour-guiding robots and of the roboticists as they develop the navigation system. They will produce an ethnographic analysis of the robots’ roles in the organization of the museums, everyday practices of museum staff and visitors, and the differential experiences of the museum space. The intellectual merits of the project consist of contributions at the intersections of STS, robotics, and human geography examining the value of ethnographic research for HRI, the development of socially-aware navigation systems, the value of a socio-spatial analytic for understanding emerging forms of robotics, and the role of robots within evolving digital geographies.
This project is jointly funded by the Science and Technology Studies program in SBE and Advancing Informal STEM Learning (AISL) Program in EHR.
The AI behind Virtual Humans Exhibit aims to communicate to the public about the capabilities and impact of artificial intelligence (AI) through AI technologies used in Virtual Humans including facial recognition and natural language processing. AI has and will continue to profoundly impact society in the United States and around the globe. It is important to prepare the nation’s youth and the future workforce with fundamental knowledge of AI. Informal settings, such as museums, offer open and flexible opportunities in helping youth and the general public learn about AI. Virtual Humans provide an ideal vehicle to illustrate many fields of AI, as AI is arguably the science of building intelligence that thinks and acts like humans. Led by a multidisciplinary team of researchers with expertise in AI, learning design, and assessment from the Institute for Creative Technologies at University of Southern California and the Lawrence Hall of Science at University of California, Berkeley, this project will develop a Virtual Human exhibit to engage visitors through structured conversations with a Virtual Human, while showcasing how AI drives the Virtual Human’s behavior behind the scenes. The exhibit will include collaborative learning experiences for visitors such as parent-child, siblings and peers to explore what AI is and is not, what AI is and is not capable of, and what impact it will have on their lives.
The project will investigate three research questions: (1) How can a museum exhibit be designed to engage visitor dyads in collaborative learning about AI? (2) How can complex AI concepts underlying the Virtual Human be communicated in a way that is understandable by the general public? And (3) How does and to what extent the Virtual Human exhibit increase knowledge and reduce misconceptions about AI?
The project leverages existing conversational Virtual Human technology developed through decades of collaborative research in AI, including machine vision, natural language processing, automated reasoning, character animation, and machine learning. Set in the informal setting of a museum, the exhibit will be designed following evidence-based research in Computer Supported Collaborative Learning. The project team will use a mixed methods design, drawing on design-based research methodologies and experimental studies. The research team will conduct analysis of visitor observations and interviews for iterative formative improvement. Randomized experimental studies will be conducted in both lab and naturalistic environments to gauge visitor knowledge about AI. Quasi-experimental analyses will be performed to study the relationship between engagement with exhibit features and AI knowledge. The project will produce an interactive exhibit with a Virtual Human installed at the Lawrence Hall of Science and other participating museums, and instruments to measure AI learning. The project will also produce a website where visitors can experience parts of the exhibit online and continue more in-depth learning about AI and the Virtual Human technology. The project holds the potential for producing theoretical and practical advances in helping the general public develop an understanding of AI capability and ethics, advancing knowledge in the process through which young learners develop knowledge about AI, and formulating design principles for creating collaborative learning experiences in informal settings. The results will be disseminated through conference presentations, scholarly publications, and social media. The Virtual Human exhibit will be designed for dissemination and made available for installations at informal science education communities.
Virtual Reality (VR) shows promise to broaden participation in STEM by engaging learners in authentic but otherwise inaccessible learning experiences. The immersion in authentic learner environments, along with social presence and learner agency, that is enabled by VR helps form memorable learning experiences. VR is emerging as a promising tool for children with autism. While there is wide variation in the way people with autism present, one common set of needs associated with autism that can be addressed with VR is sensory processing. This project will research and model how VR can be used to minimize barriers for learners with autism, while also incorporating complementary universal designs for learning (UDL) principles to promote broad participation in STEM learning. 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. This project will build on a prototype VR simulation, Mission to Europa Prime, that transports learners to a space station for exploration on Jupiter's moon Europa, a strong candidate for future discovery of extraterrestrial life and a location no human can currently experience in person. The prototype simulation will be expanded to create a full, immersive STEM-based experience that will enable learners who often encounter cognitive, social, and emotional barriers to STEM learning in public spaces, particularly learners with autism, to fully engage and benefit from this STEM-learning experience. The simulation will include a variety of STEM-learning puzzles, addressing science, mathematics, engineering, and computational thinking through authentic and interesting problem-solving tasks. The project team's learning designers and researchers will co-design puzzles and user interfaces with students at a post-secondary institute for learners with autism and other learning differences. The full VR STEM-learning simulation will be broadly disseminated to museums and other informal education programs, and distributed to other communities.
Project research is designed to advance knowledge about VR-based informal STEM learning and the affordances of VR to support learners with autism. To broaden STEM participation for all, the project brings together research at the intersection of STEM learning, cognitive and educational neuroscience, and the human-technology frontier. The simulation will be designed to provide agency for learners to adjust a STEM-learning VR experience for their unique sensory processing, attention, and social anxiety needs. The project will use a participatory design process will ensure the VR experience is designed to reduce barriers that currently exclude learners with autism and related conditions from many informal learning opportunities, broadening participation in informal STEM learning. Design research, usability, and efficacy studies will be conducted with teens and adults at the Pacific Science Center and Boston Museum of Science, which serve audiences with autism, along with the general public. Project research is grounded in prior NSF-funded research and leverages the team's expertise in STEM learning simulations, VR development, cognitive psychology, universal design, and informal science education, as well as the vital expertise of the end-user target audience, learners with autism. In addition to being shared at conferences, the research findings will be submitted for publication to peer-reviewed journals for researchers and to appropriate publications for VR developers and disseminators, museum programs, neurodiverse communities and other potentially interested parties.
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:
Teon EdwardsJodi Asbell-ClarkeJamie LarsenIbrahim Dahlstrom-Hakki
Sense-making with data through the process of visualization—recognizing and constructing meaning with these data—has been of interest to learning researchers for many years. Results of a variety of data visualization projects in museums and science centers suggest that visitors have a rudimentary understanding of and ability to interpret the data that appear in even simple data visualizations. This project supports the need for data visualization experiences to be appealing, accommodate short and long-term exploration, and address a range of visitors’ prior knowledge. Front-end evaluation
Increasingly, scientists and their institutions are engaging with lay audiences via media. The emergence of social media has allowed scientists to engage with publics in novel ways. Social networking sites have fundamentally changed the modern media environment and, subsequently, media consumption habits. When asked where they primarily go to learn more about scientific issues, more than half of Americans point to the Internet. These online spaces offer many opportunities for scientists to play active roles in communicating and engaging directly with various publics. Additionally, the proposed research activities were inspired by a recent report by the National Academies of Sciences, Engineering, and Medicine that included a challenge to science communication researchers to determine better approaches for communicating science through social media platforms. Humor has been recommended as a method that scientists could use in communicating with publics; however, there is little empirical evidence that its use is effective. The researchers will explore the effectiveness of using humor for communicating about artificial intelligence, climate science and microbiomes.
The research questions are: How do lay audiences respond to messages about scientific issues on social media that use humor? What are scientists' views toward using humor in constructing social media messages? Can collaborations between science communication scholars and practitioners facilitate more effective practices? The research is grounded in the theory of planned behavior and framing as a theory of media effects. A public survey will collect and analyze data on Twitter messages with and without humor, the number of likes and re-tweets of each message, and their scientific content. Survey participants will be randomly assigned to one of twenty-four experimental conditions. The survey sample, matching recent U.S. Census Bureau data, will be obtained from opt-in panels provided by Qualtrics, an online market research company. The second component of the research will quantify the attitudes of scientists toward using humor to communicate with publics on social media. Data will be collected from a random sample of scientists and graduate students at R1 universities nationwide. Data will be analyzed using descriptive statistics and regression modeling.
The broader impacts of this project are twofold: findings from the research will be shared with science communication scholars and trainers advancing knowledge and practice; and an infographic (visual representation of findings) will be distributed to practitioners who participate in research-practice partnerships. It will provide a set of easily-referenced, evidence-based guidelines about the types of humor to which audiences respond positively on social media.
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.
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TEAM MEMBERS:
Sara YeoLeona Yi-Fan SuMichael Cacciatore
Cities and communities in the U.S. and around the world are entering a new era of transformational change, in which their inhabitants and the surrounding built and natural environments are increasingly connected by smart technologies, leading to new opportunities for innovation, improved services, and enhanced quality of life. The Smart and Connected Communities (SCC) program supports strongly interdisciplinary, integrative research and research capacity-building activities that will improve understanding of smart and connected communities and lead to discoveries that enable sustainable change to enhance community functioning. This project is a Research Coordination Network (RCN) that focuses on achieving SCC for medium/small size, remote, and rural communities through a polycentric (multiple centers) integrated policy, design, and technology approach. The communities served by the RCN have higher barriers to information, resources, and services than larger urban communities. To reduce this gap, the PIs propose to develop need-based R&D pipelines to select solutions with the highest potential impacts to the communities. Instead of trying to connect under-connected communities to nearby large cities, this proposal aims to develop economic opportunities within the communities themselves. This topic aligns well with the vision of the SCC program, and the proposed RCN consists of a diverse group of researchers, communities, industry, government, and non-profit partners.
This award will support the development of an RCN within the Commonwealth of Virginia which will coordinate multiple partners in developing innovations utilizing smart and connected technologies. The goal of the research coordination network is to enable researchers and citizens to collaborate on research supporting enhanced quality of life for medium, small, and rural communities which frequently lack the communication and other infrastructure available in cities. The research coordination network will be led by the University of Virginia. There are 14 partner organizations including six research center partners in transportation, environment, architecture and urban planning, and engineering and technology; two State and Industry partners (Virginia Municipal League and Virginia Center for Innovative Technology); four community partners representing health services (UVA Center for Telemedicine), small and remote communities (Weldon Cooper Center), neighborhood communities (Charlottesville Neighborhood Development), and urban communities (Thriving Cities); and two national partners which support high speed networking (US-Ignite) and city-university hubs (MetroLab). Examples of research coordination include telemedicine services, transportation services, and user-centric and community-centric utilization and deployment of sensor technologies.
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TEAM MEMBERS:
Ila BermanT. Donna ChenKaren RheubanQian Cai
Increasingly, the prosperity, innovation and security of individuals and communities depend on a big data literate society. Yet conspicuously absent from the big data revolution is the field of teaching and learning. The revolution in big data must match a complementary revolution in a new kind of literacy, through a significant infusion of STEM education with the kinds of skills that the revolution in 21st century data-driven science demands. This project represents a concerted effort to determine what it means to be a big data literate citizen, information worker, researcher, or policymaker; to identify the quality of learning resources and programs to improve big data literacy; and to chart a path forward that will bridge big data practice with big data learning, education and career readiness.
Through a process of inquiry research and capacity-building, New York Hall of Science will bring together experts from member institutions of the Northeast Big Data Innovation Hub to galvanize big data communities of practice around education, identify and articulate the nature and quality of extant big data education resources and draft a set of big data literacy principles. The results of this planning process will be a planning document for a Big Data Literacy Spoke that will form an initiative to develop frameworks, strategies and scope and sequence to advance lifelong big data literacy for grades P-20 and across learning settings; and devise, implement, and evaluate programs, curricula and interventions to improve big data literacy for all. The planning document will articulate the findings of the inquiry research and evaluation to provide a practical tool to inform and cultivate other initiatives in data literacy both within the Northeast Big Data Innovation Hub and beyond.
This project takes an ethnographic and design-based approach to understanding how and what people learn from participation in makerspaces and explores the features of those environments that can be leveraged to better promote learning. Makerspaces are physical locations where people (often families) get together to make things. Some participants learn substantial amounts of STEM content and practices as they design, build, and iteratively refine working devices. Others, however, simply take a trial and error approach. Research explores the affordances are of these spaces for promoting learning and how to integrate technology into these spaces so that they are transformed from being makerspaces where learning happens, but inconsistently, into environments where learning is a consistent outcome of participation. One aim is to learn how to effectively design such spaces so that participants are encouraged and helped to become intentional, reflective makers rather than simply tinkerers. Research will also advance what is known about effective studio teaching and learning and advance understanding of how to support youth to help them become competent, creative, and reflective producers with technology(s). The project builds on the Studio Thinking Framework and what is known about development of meta-representational competence. The foundations of these frameworks are in Lave and Wengers communities of practice and Rogoff's, Stevens et al.'s, and Jenkins et al.'s further work on participatory cultures for social networks that revolve around production. A sociocultural approach is taken that seeks to understand the relationships between space, participants, and technologies as participants set and work toward achieving goals. Engaging more of our young population in scientific and technological thinking and learning and broadening participation in the STEM workplace are national imperatives. One way to address these imperatives is to engage the passions of young people, helping them recognize the roles STEM content and practices play in achieving their own personal goals. Maker spaces are neighborhood spaces that are arising in many urban areas that allow and promote tinkering, designing, and construction using real materials, sometimes quite sophisticated ones. Participating in designing and successfully building working devices in such spaces can promote STEM learning, confidence and competence in one's ability to solve problems, and positive attitudes towards engineering, science, and math (among other things). The goal in this project is to learn how to design these spaces and integrate learning technologies so that learning happens more consistently (along with tinkering and making) and especially so that they are accessible and inviting to those who might not normally participate in these spaces. The work of this project is happening in an urban setting and with at-risk children, and a special effort is being made to accommodate making and learning with peers. As with Computer Clubhouses, maker spaces hold potential for their participants to identify what is interesting to them at the same time their participation gives them the opportunity to express themselves, learn STEM content, and put it to use.
Through a comparative case study, Sheridan and colleagues explore how makerspaces may function as learning environments. Drawing on field observations, interviews, and analysis of artifacts, videos, and other documents, the authors describe features of three makerspaces and how participants learn and develop through complex design and making practices. They describe how the makerspaces help individuals identify problems, build models, learn and apply skills, revise ideas, and share new knowledge with others. The authors conclude with a discussion of the implications of their findings for this