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 paper contributes a theoretical framework informed by historical, philosophical and ethnographic studies of science practice to argue that data should be considered to be actively produced, rather than passively collected. We further argue that traditional school science laboratory investigations misconstrue the nature of data and overly constrain student agency in their production. We use our “Data Production” framework to analyze activity of and interviews with high school students who created data using sensors and software in a ninth-grade integrated science class. To understand the
Craft has emerged as an important reference point for human-computer interaction (HCI). To avoid a misrepresenting, all-encompassing application of craft to interaction design, this position paper first discerns craft from HCI. It develops material engagement and mediation as differentiating factors to reposition craft in relation to tangible interaction design. The aim is to clarify craft’s relation to interaction design and to open up new opportunities and questions that follow from this repositioning.
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
This Research Advanced by Interdisciplinary Science and Engineering (RAISE) project is supported by the Division of Research on Learning in the Education and Human Resources Directorate and by the Division of Computing and Communication Foundations in the Computer and Information Science and Engineering Directorate. This interdisciplinary project integrates historical insights from geometric design principles used to craft classical stringed instruments during the Renaissance era with modern insights drawn from computer science principles. The project applies abstract mathematical concepts toward the making and designing of furniture, buildings, paintings, and instruments through a specific example: the making and designing of classical stringed instruments. The research can help instrument makers employ customized software to facilitate a comparison of historical designs that draws on both geometrical proofs and evidence from art history. The project's impacts include the potential to shift in fundamental ways not only how makers think about design and the process of making but also how computer scientists use foundational concepts from programming languages to inform the representation of physical objects. Furthermore, this project develops an alternate teaching method to help students understand mathematics in creative ways and offers specific guidance to current luthiers in areas such as designing the physical structure of a stringed instrument to improve acoustical effect.
The project develops a domain-specific functional programming language based on straight-edge and compass constructions and applies it in three complementary directions. The first direction develops software tools (compilers) to inform the construction of classical stringed instruments based on geometric design principles applied during the Renaissance era. The second direction develops an analytical and computational understanding of the art history of these instruments and explores extensions to other maker domains. The third direction uses this domain-specific language to design an educational software tool. The tool uses a calculative and constructive method to teach Euclidean geometry at the pre-college level and complements the traditional algebraic, proof-based teaching method. The representation of instrument forms by high-level programming abstractions also facilitates their manufacture, with particular focus on the arching of the front and back carved plates --- of considerable acoustic significance --- through the use of computer numerically controlled (CNC) methods. The project's novelties include the domain-specific language itself, which is a programmable form of synthetic geometry, largely without numbers; its application within the contemporary process of violin making and in other maker domains; its use as a foundation for a computational art history, providing analytical insights into the evolution of classical stringed instrument design and its related material culture; and as a constructional, computational approach to teaching geometry.
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.
African American and Latinx youth are often socialized towards athletic activity and sports participation, sometimes at the expense of their exploration of the range of potential career paths including those in the science, technology, engineering, and mathematics (STEM) fields. This project will immerse middle school youth in the rapidly growing world of sports data analytics and build their knowledge of statistics concepts and the data science process. The project will focus on the STEM interests and knowledge development of African American and Latinx youth, an underrepresented and underserved group in STEM. Researchers will explore the ways youths' social identities can and should serve as bridges towards future productive academic and professional identities including those associated with STEM learning and the STEM professions. The outcomes of the project will advance knowledge in promoting elements of informal learning experiences that build adolescents' motivation and persistence for productive participation in STEM courses and careers. This project is funded by the Advancing Informal STEM Learning program (AISL), which seeks to advance new approaches to and evidence-based understanding of the design and development of STEM learning opportunities for the public in informal environments, and the Innovative Technology Experiences for Students and Teachers program (ITEST), which funds projects that leverage innovative uses of technologies to prepare diverse youth for the STEM workforce, with a focus on broadening participation among underrepresented and underserved groups in STEM fields.
Over a three-year period, 250 middle school learners in the West Baltimore, Maryland and Hyattsville, Maryland areas will engage in three main learning activities: Summer Camp (three weeks), Sports Day Saturdays, and a Spring Summit. Through a partnership between the University of Maryland and Coppin State University, the project will utilize resources in multiple departments and units across both universities, and engage with youth sports leagues such as the American Athletic Union (AAU) to support participants' engagement in the data science process including collection of raw data, exploration of data, development of models, visualization, communication, and reporting of data, and data-driven decision making. Furthermore, youth participants will attend local AAU, college, and professional sporting events, and interact with members of coaching staffs to better understand the ways performance data technologies are utilized to inform recruitment and team performance. The mixed-methods research agenda for this project is guided by three main questions: (1) What elements of the project's model are most successful at supporting congruence of adolescents' academic identity, including STEM identity and social identity including athletic identity? (2) What elements support adolescents' motivation, and persistence for productive participation in current and future STEM courses? (3) To what extent did the project appear to influence participants' perceptions of their future professions? At multiple points throughout the experience, participants will complete surveys designed to document and assess statistics and data science knowledge; interest in STEM careers; academic, social and athletic identity development; and STEM course taking patterns. Researchers will also observe project activities, interview a focal group of participants, and survey participants' parents to identify elements of learning experiences that encourage and support adolescents' interest in STEM disciplines and STEM professions. The project team will develop conceptual and pedagogical frameworks that support middle school youth' engagement and interest in science, engineering, technology, and mathematics through repurposing spaces where these youths frequent. A major outcome of the project will be workforce preparation and offers a promising approach for encouraging youth to persist along STEM pathways, which may ultimately result in broadened participation in STEM workforces.
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.
Despite the ubiquity of Artificial Intelligence (AI), public understanding of how it works and is used is limited This project will research, design, and develop innovative approaches focusing on Artificial Intelligence (AI) for under-represented youth ages 14-24. Program components include live social media chats with AI leaders, app development, journalistic investigations of ethical issues in machine learning, and review of AI-based consumer products. Youth Radio is a non-profit media and tech organizations that provides youth with skills in STEM, journalism, arts, and communications. They engage 250 youth annually through free after-school classes and work shifts. Participants are 90% youth of color and 80% low income. Project partners include the MIT Media Lab which developed App Inventor which allows novice users to build fully functional apps. Staff from Google will serve as a project advisor on the curriculum. The project has exceptional national reach through the dissemination of its media and apps through national outlets such as NPR and Teen Vogue as well as various platforms including online, on-air, as well as presentations, publications, and training tools. The project broadens participation by engaging these low income youth of color in developing skills critical to the workforce of the future. It will help prepare an upcoming generation of Artificial Intelligence creators, users, and consumers who understand the technology and embrace and encourage its potential.It will give them the necessary knowledge and opportunities for careers in an AI-driven future.
This project is grounded in sociocultural learning theory and practice and is interdisciplinary by design. The theoretical framework holds that Computational Thinking plus Critical Pedagogy leads to Critical Computational Literacy. Also, Digital Age Civics plus Participatory Culture leads to Civic Imagination helping youth build a better world through technology. The driving research questions include: What do underrepresented youth understand about AI and its role in society? What are the ethical dilemmas posed by AI from their vantage point? What are the features of an engaging ethics-centered pedagogy with AI? What impact do the AI products developed by the youth have on the target audience? The research design will use ethnographic techniques and design research to study and analyze youth learning. Data sources will include baseline surveys, audio recordings and transcriptions from learning sessions with the participants, research analytic memos, focus group interviews, student-generating artifacts of learning and finished products, etc. The design-based approach will enable systematic, evidence-based iteration on the initiative's activities, pedagogical approach and products. An independent summative evaluation will provide complementary data and perspective to triangulate with the research findings.
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:
Elisabeth SoepEllin O'LearyHarold Abelson
Research that seeks to understand classroom interactions often relies on video recordings of classrooms so that researchers can document and analyze what teachers and students are doing in the learning environment. When studies are large scale, this analysis is challenging in part because it is time-consuming to review and code large quantities of video. For example, hundreds of hours of videotaped interaction between students working in an after-school program for advancing computational thinking and engineering learning for Latino/a students. This project is exploring the use of computer-assisted methods for video analysis to support manual coding by researchers. The project is adapting procedures used for computer-aided diagnosis systems for medical systems. The computer-assisted process creates summaries that can then be used by researchers to identify critical events and to describe patterns of activities in the classroom such as students talking to each other or writing during a small group project. Creating the summaries requires analyzing video for facial recognition, motion, color and object identification. The project will investigate what parts of student participation and teaching can be analyzed using computer-assisted video analysis. This project is supported by NSF's EHR Core Research (ECR) program, the STEM+C program and the AISL program. The ECR program emphasizes fundamental STEM education research that generates foundational knowledge in the field. The project is funded by the STEM+Computing program, which seeks to address emerging challenges in computational STEM areas through the applied integration of computational thinking and computing activities within disciplinary STEM teaching and learning in early childhood education through high school (preK-12). As part of its overall strategy 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 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.
The video analysis systems will provide video summarizations for specific activities which will allow researchers to use these results to quantify student participation and document teaching practices that support student learning. This will support the analysis of large volumes of video data that are often time-consuming to analyze. The video analysis system will identify objects in the scene and then use measures of distances between objects and other tracking methods to code different activities (e.g., typing, talking, interaction between the student and a facilitator). The two groups of research questions are as follows. (1) How can human review of digital videos benefit from computer-assisted video analysis methods? Which aspects of video summarization (e.g., detected activities) can help reduce the time it takes to review the videos? Beyond audio analytics, what types of future research in video summarization can help reduce the time that it takes to review videos? (2) How can we quantify student participation using computer-assisted video analysis methods? What aspects of student participation can be accurately measures by computer-assisted video analysis methods? The video to be used for this study is drawn from a project focused on engineering and computational thinking learning for Latino/a students in an after-school setting. Hundreds of hours of video are available to be reviewed and analyzed to design and refine the system. The resulting coding will also help document patterns of engagement in the learning environment.
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:
Marios PattichisSylvia Celedon-PattichisCarlos LopezLeiva
This guide describes what took place during NYSCI’s Big Data for Little Kids workshop series, Museum Makers: Designing With Data. In addition to detailed outlines of the activities implemented during the program, this guide includes a glossary of recurrent terms and resources used throughout the workshops.
In 2017, as part of a National Science Foundation funded project, the New York Hall of Science (NYSCI) set out to teach Big Data concepts to children ages 4 – 8 years old. NYSCI developed and piloted an after-school program for families to utilize the data cycle as a method of informed
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.