Peer production projects involve people in many tasks, from editing articles to analyzing datasets. To facilitate mastery of these practices, projects offer a number of learning resources, ranging from project-defined FAQsto individually-oriented search tools and communal discussion boards. However, it is not clear which project resources best support participant learning, overall and at different stages of engagement. We draw on Sørensen's framework of forms of presence to distinguish three types of engagement with learning resources: authoritative, agent-centered and communal. We assigned
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TEAM MEMBERS:
Corey Brian JacksonCarsten OsterlundKevin CrowstonMahboobeh HarandiLaura Trouille
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
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 study examines the relative efficacy of citizen science recruitment messages appealing to four motivations that were derived from previous research on motives for participation in citizen-science projects. We report on an experiment (N=36,513) that compared the response to email messages designed to appeal to these four motives for participation. We found that the messages appealing to the possibility of contributing to science and learning about science attracted more attention than did one about helping scientists but that one about helping scientists generated more initial
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TEAM MEMBERS:
Tae Kyoung LeeKevin CrowstonMahboobeh HarandiCarsten ØsterlundGrant Miller
This Advancing Informal Science Learning Pathways project, Using Technology to Research After Class (UTRAC), explores whether a combination of technology (e.g., iPad-enabled sensors, web-based inquiry-focused portal) and facilitated visits improves learning outcomes for rural and Native American elementary-age youth in after school programs. Expected outcomes include improved engagement, knowledge, skills, and attitudes toward science, technology, engineering, and math (STEM). Project goals include promoting STEM learning through science inquiry activities keyed to specific Next Generation Science Standards as well as improving how technology can be used to enhance learning outcomes in afterschool programs. The experimental design of this project - testing the effects of physical or virtual facilitation visits on learning outcomes - will lead to improvements in STEM learning outcomes among rural and underrepresented students. This project will employ several innovations in utilizing technology to teach STEM topics including: (i) hands-on, real-time, crowd sourced data collected by participants in their schoolyards; (ii) a pedagogic emphasis on communication of schoolyard data among and between participants; (iii) testing of motivational incentives; and (iv) partnerships between after school providers, preservice teachers, and university researchers as facilitators. The entire process will be modularized so that it can be modified in terms of place, STEM topic or student cohort. The topic focus of the project -- Life Under Snow -- is relevant to participating students, as Montana school playgrounds lie blanketed under snow for the majority of the school year; it includes elements of snow science, carbon cycle science, and a combination at the intersection of three recent literacy initiatives (e.g., Earth Science, Climate, or Energy). UTRAC will pilot and evaluate facilitated snow science/carbon cycle science activities that couple real-time schoolyard data with tools patterned after those available through WISE (Web-based Inquiry Science Environment; wise.berkeley.edu). Participants will collect and compare data with other youth participants, and researchers will use formative assessments to define interventions with potential to maximize student engagement and learning improvements among underserved youth. The project will advance understanding of informal education's potential to improve STEM engagement, knowledge, skills and attitudes by quantifying how - and to what extent - youth engage with emerging technologies iPad-enabled sensors, and crowdsourcing and visualization tools. The deliverables include a quantifying metric for learning outcomes, a training model for the iPad sensors and web application, an orientation kit, a social media portal, and database for the measurements.
This article explores the roots of the citizen science movement. It uses several ongoing projects as examples, including the Audubon's Christmas Bird Count, research into bee colony collapse, and nanotechnology programs. The article concludes by providing guidance for the development of future citizen science projects, focusing on an increased dialogue between traditional and informal science education.
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TEAM MEMBERS:
Michael MuellerDeborah TippinsLynn Bryan
To quantify how much of the night sky has been lost to light pollution, students in grades 3-8 compare their backyard view of Orion to six star charts of the constellation with varying limiting magnitudes. Using thousands of observations from across the local community, teams of students from individual schools plot the collective results by constructing a 3D model out of LEGO blocks. Beforehand, all teachers integrate some aspect of the topic in their regular classroom instruction. The website offers supporting lessons, resources, and ideas for teachers and families alike.
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Penn-Harris-Madison School Corporation
This report summarizes evaluative findings from a project titled “What Curiosity Sounds Like: Discovering, Challenging, and Sharing Scientific Ideas” (a.k.a.: “Discovery Dialogues”). The project, a Full-Scale development project funded by the National Science Foundation as part of its Advancing Informal Science Learning (AISL) program, explored new ways to actively engage both lay and professional audiences, and foster meaningful communication between scientists and the general public. Appendix includes survey and interview questions.