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
Historic art objects provide a collection of materials that have been naturally aged for decades or even centuries. In addition to the intrinsic archival value of these materials, they are also models for understanding property degradation over long periods of time. This project aims to develop computational and experimental tools needed to understand how these changes take place. To accomplish this task a research network has been established between Northwestern University and leaders in cultural heritage science from the Rijksmuseum and the University of Amsterdam in the Netherlands, the National Research Council in Italy, and the Synchrotron Soleil in France. This new infrastructure promises to deliver a significant enhancement of research and education resources (networks, partnership and increased access to facilities and instrumentation) to a diverse group of users. The art objects central to the project provide a series of well-defined case studies for investigating complex materials systems that are both applicable to materials education and push the limits of the existing analytical tools, thus inspiring instrumental innovations across broad sectors of the physical sciences. Further development of these tools will enable art conservators to more effectively make informed decisions about treatments of works of art, and to understand long-term materials degradation more generally. The project will also deliver a significant enhancement of research and education infrastructure by a diverse group of users and will provide meaningful, international research experience to 50 participants, with a strong emphasis on scientists at the beginning of their careers. In addition, the connections between science and art will illustrate the creative aspects of both disciplines to a very broad audience, attracting a more representative cross section of people into science.
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TEAM MEMBERS:
Kenneth ShullFrancesca CasadioOliver CossairtAggelos KatsaggelosMarc Walton
resourceprojectProfessional Development, Conferences, and Networks
The Center for Integrated Quantum Materials pursues research and education in quantum science and technology. With our research and industry partners, the Museum of Science, Boston collaborates to produce public engagement resources, museum programs, special events and media. We also provide professional development in professional science communication for the Center's students, post-docs, and interns; and coaching in public engagement. The Museum also sponsors The Quantum Matters(TM) Science Communication Competition (www.mos.org/quantum-matters-competition) and NanoDays with a Quantum Leap. In association with CIQM and IBM Q, the Museum hosted the first U.S. museum exhibit on quantum computing.
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TEAM MEMBERS:
Robert WesterveltCarol Lynn AlpertRay AshooriTina Brower-Thomas
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
The rise of artificial intelligence has recently led to bots writing real news stories about sports, finance and politics. As yet, bots have not turned their attention to science, and some people still mistakenly think science is too complex for bots to write about. In fact, a small number of insiders are now applying AI algorithms to summarise scientific research papers and automatically turn them into simple press releases and news stories. Could the science beat be next in line for automation, potentially making many science reporters --- and even editors --- superfluous to science
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
In partnership with the Digital NEST, students engage in near to peer learning with a technical tool for the benefit of a nonprofit that tackles issues the youth are passionate about. Youth build first from an 'internal’ Impactathon, to planning and developing an additional Impactathon for a local partner and then traveling to another partner elsewhere in the state. Participants range from 14 to 24 from UC Santa Cruz students to middle schoolers from Watsonville and Salinas.
This poster was presented at the 2019 AISL Principal Investigators Meeting.
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TEAM MEMBERS:
Amber Holguin
resourceresearchProfessional Development, Conferences, and Networks
A group of 12 researchers gathered at MSI to discuss issues surrounding the critical but challenging area of how to measure the long-term effects or impacts of ISE experiences, chaired by Dr. John Falk, Oregon State University and Institute for Learning Innovation, and hosted by Aaron Price, Museum of Science & Industry, Chicago, (MSI) on July 18, 2018. All the invitees had expertise and experience in this area, resulting in a rich conversation based equally in theory and practice. The goal of the discussion was to build on the collective experience of the group to identify and address key
This pilot study will examine the effectiveness of an innovative applied social change, community and technology based program on marginalized youths' access, interest, efficacy and motivation to learn and engage in digital technology applications. Using stratified near-peer and peer-to-peer mentoring approaches, the pilot builds on extant literature that indicates that peer-supported hands-on mentoring and experiences can alleviate some barriers to youth engagement in digital technologies, particularly among underrepresented groups. In this project, undergraduate students will mentor and work collaboratively with high school youth primarily of Hispanic descent and community-based organizations to develop creative technology-based solutions to address social issues and challenges within their local communities, culminating in events called Impactathons. These community-hosted local and state-wide events set this pilot project apart from similar work in the field. The Impactathons not only provide a space for intellectual discourse and problem-solving among the undergraduate-youth-community partners but the Impactathons will also codify expertise from scientists, social scientists, technologists, community leaders, and other stakeholders to develop technology-based solutions with real world application. If successful, a distal outcome will be increased youth interest in digital technologies and related fields. In the short term, favorable findings will provide preliminary evidence of success and lay the foundation for a more extensive study in the future.
This pilot project is a collaboration between the Everett Program, a student-led program for Technology and Social Change at the University of California Santa Cruz - a Hispanic Serving Institution - and the Digital NEST, a non-profit, high-tech youth career development and collaboration space for young people ages 14-24. Through this partnership and other recruitment efforts, an estimated 70-90 individuals will participate in the Impactathon pilot program over two years. Nearly two-thirds of the participants are expected to be undergraduate students. They will receive extensive training in near-peer and peer-to-peer mentoring and serve as mentors for and co-innovation developers with the high school youth participants. The undergraduates and youth will partner with local community organizations to identify a local social challenge that can be addressed through a technology-based solution. The emergent challenges will vary and could span the spectrum of STEM and applied social science topics of interest. Working in informal contexts (i.e., afterschool. weekend), the undergraduate-youth-community partner teams will work collaboratively to develop practical technology-based solutions to real world challenges. The teams will convene three times per year, locally and statewide, at student and community led Impactathons to share their work and glean insights from other teams to refine their innovations. In parallel, the research team will examine the effectiveness of the Impactathon model in increasing the undergraduate and youths' interest, motivation, excitement, engagement and learning of digital technologies. In addition to the research, the formative and summative evaluations should provide valuable insights on the effectiveness of the model and its potential for expansion and replication.
The project is co-funded by the Advancing Informal STEM Learning (AISL) Program and STEM +C. The AISL program seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. STEM + C focuses on research and development of interdisciplinary and transdisciplinary approaches to the integration of computing within STEM teaching and learning for preK-12 students in both formal and informal settings.
This 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.
This project tackles the urgent needs of the nation to engage people of all ages in computational thinking and help them learn basic computer science concepts with a unique and innovative approach of structured in-game computer program coding. Researchers will explore the design and development of a 3D puzzle-based game, called May's Journey, in which players solve an environmental maze by using the game's pseudo code to manipulate game objects. The game is designed to teach introductory but foundational concepts of computer programming including abstraction, modularity, reusability, and debugging by focusing players on logic and concepts while asking them to type simple instructions in a simplified programming language designed for novices. The game design in this project differs from today's block-based programming learning approaches that are often too far from actual computer code, and also differs from professional programming languages which are too complex for novices. The game and its embedded programming language learning are designed to be responsive to the progress of the learner throughout the game, transitioning from pseudo code to the embedded programming language itself. Error messages for debugging are also designed to be adaptive to players' behavior in the game. Using extensive log data collected from people playing the game, researchers can study how people learn computer programming. Such knowledge can advance understanding of the learning processes in computer programming education. Additionally, this work emphasizes the use of games as informal learning environments as they are accessible and fun, drawing attention and retention of many learners of different age groups with the potential to change attitudes towards computer programming across different populations. This project is co-funded by the STEM + Computing (STEM+C) program that supports research and development to understand the integration of computing and computational thinking in STEM learning, and the Advancing Informal STEM Learning (AISL) program that funds innovative research, approaches and resources for use in a variety of settings with its overall strategy to enhance learning in informal environments.
The project's formative and summative evaluation methods, including surveys, expert reviews of learners' computer code developed in the game, and interviews, are used to gauge learners' engagement as well as learning. In exploring learning, researchers aim to understand how players build implicit computer science knowledge through gameplay and how that gameplay relates to their performance on external transfer tasks. The project will answer the following three research questions: (1) Can observers reliably detect and label patterns of gameplay that provide evidence of learning or misconceptions regarding the four computer science constructs - abstraction, modularity, debugging and semantics - that learners exhibit playing May's Journey? (2) How does learner's implicit knowledge of these computer science constructs change over time and do those patterns vary by gender and prior programming experiences? (3) Is there a strong correlation between implicit learning measures and transfer of CS concepts: modularity, debugging, semantics, and abstraction? How do these correlations vary across elements of the game? This work will result in several outcomes: game design metaphors tested for their learning and engagement value that can be abstracted and embedded in different games. This project will also contribute patterns and an understanding of how people learn and engage in problem solving using concepts of abstraction, modularity, debugging and semantics. These outcomes will lead to advancement in knowledge in the learning sciences as well as the design of educational games that enrich STEM learning, particularly in programming and computational thinking. In addition, this project will engage female participants and underserved populations through partnering organizations including National Girls Collaborative project.
This 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.