Counterspaces in science, technology, engineering, and mathematics (STEM) are often considered “safe spaces” at the margins for groups outside the mainstream of STEM education. The prevailing culture and structural manifestations in STEM have traditionally privileged norms of success that favor competitive, individualistic, and solitary practices—norms associated with White male scientists. This privilege extends to structures that govern learning and mark progress in STEM education that have marginalized groups that do not reflect the gender, race, or ethnicity conventionally associated with
DATE:
TEAM MEMBERS:
Maria OngJanet SmithLily Ko
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.
DATE:
-
TEAM MEMBERS:
Robert WesterveltCarol Lynn AlpertRay AshooriTina Brower-Thomas
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
DATE:
-
TEAM MEMBERS:
Vassiliki KalogeraAggelos KatsaggelosKevin CrowstonLaura TrouilleJoshua SmithShane LarsonLaura Whyte
This project, an NSF INCLUDES Design and Development Launch Pilot, managed by the University of Nevada, Reno, addresses the grand challenge of increasing underrepresentation regionally in the advanced manufacturing sector. Using the state's Learn and Earn Program Advanced Career Pathway (LEAP) as the foundation, science, technology, engineering and mathematics (STEM) activities will support and prepare Hispanic students for the region's workforce in advanced manufacturing which includes partnerships with Truckee Meadows Community College (TMCC), the state's Governor's Office of Economic Development, Charles River Laboratories, Nevada Established Program to Stimulate Competitive Research (Nevada EPSCoR) and the K-12 community.
The expected outcomes from the project will inform the feasibility, expandability and transferability of the LEAP framework in diversifying the state's workforce locally and the STEM workforce nationally. Formative and summative evaluation will be conducted with a well-matched comparison group. Dissemination of project results will be disseminated through the Association for Public Land-Grant Universities (APLU), STEM conferences and scholarly journals.
DATE:
-
TEAM MEMBERS:
David ShintaniJulie EllsworthKarsten HeiseRobert StachlewitzRegina Tempel
resourceprojectProfessional Development, Conferences, and Networks
The goal of FLIP (Diversifying Future Leadership in the Professoriate), an NSF INCLUDES Design and Development Launch Pilot, is to address the broadening participation challenge of increasing the diversity of the future leadership in the professoriate in computing at research universities as a way to achieve diversity across the field. According to the 2016 CRA Taulbee Survey, only 4.3% of the tenure-track faculty at PhD-granting universities are from underrepresented minorities. This challenge is important to address because diverse faculty contributes to academia in the following critical ways: serve as excellent role models for a diverse study body, bring diverse backgrounds to the student programs and policies developed by the department, and bring diverse perspectives to the research projects and programs. Further, the focus is on research universities, because in practice, key national leadership roles, such as serving on national committees that impact thefield of computing, often come from research universities.
The shared purpose and broad vision of the FLIP launch pilot is to increase faculty diversity in computing at research universities by increasing the diversity of PhD graduates from the top producers of computing faculty. The focus is on four underrepresented groups in computing: African Americans; Hispanics; Native Americans and indigenous peoples; and Persons with Disabilities. The long-term goal is to pursue this vision through strategic partnerships with those institutions that are the top producers of computing faculty and organizations that focus on diverse students in STEM, as well as partnerships that collectively adopt proven strategies for recruiting, graduating, and preparing a diverse set of doctoral students for academic careers. The purpose of the pilot is to establish a unified approach across the different partners that will build upon proven strategies to develop novel practices for increasing the diversity of the PhD graduates from key institutions, thereby increasing the faculty diversity in computing at research universities. For the pilot, FLIP will focus on recruitment and admissions and professional development for current PhD students.
DATE:
-
TEAM MEMBERS:
Valerie TaylorCharles IsbellJeffrey ForbesUniversity of Chicago
This NSF INCLUDES Design and Development Launch Pilot (named ALCSE-INCLUDES) project will develop and implement an innovative computer science (CS) education model that will provide all 8th grade students in 3 districts in Alabama's 'Black Belt' with exciting and structured hands-on activities intended to make CS learning enjoyable. The course will use an educational style called "learning CS by making" where students will create a CS-based product (such as a robot) and understand the concepts that make the product work. This hands-on approach has the potential to motivate diverse student populations to pursue higher level CS courses and related disciplines during and after high school, and to join the CS workforce, which is currently in need of more qualified workers.
ALCSE-INCLUDES Launch Pilot will unite the efforts of higher education institutions, K-12 officials, Computer Science (CS)-related industry, and community organizations to pursue a common agenda: To develop, implement, study, and evaluate a scalable and sustainable prototype for CS education at the middle school level in the Alabama Black Belt (ABB) region. The ABB is a region with a large African-American, low-income population; thus, the program will target individuals who have traditionally had little access to CS education. The prototype for CS education will be piloted with 8th grade students in 3 ABB schools, using a set of coordinated and mutually reinforcing activities that will draw from the strengths of all members of the ALCSE Alliance. The future scaled-up version of the program will implement the prototype in the 73 middle schools that comprise ALL 19 school districts of the ABB. The program's main innovation is to provide CS education using a makerspace, a dedicated area equipped with grade-appropriate CS resources, in which students receive mentored and structured hands-on activities. The goal is to engage ALL students, in learning CS through making, an evidence-based pedagogical approach expected to reinforce skills and promote deep interest in CS.
DATE:
-
TEAM MEMBERS:
Shaik JeelaniBruce CrawfordMohammed QaziJeffrey GrayJacqueline Brooks
The Sustainability Teams Empower and Amplify Membership in STEM (S-TEAMS), an NSF INCLUDES Design and Development Launch Pilot project, will tackle the problem of persistent underrepresentation by low-income, minority, and women students in STEM disciplines and careers through transdisciplinary teamwork. As science is increasingly done in teams, collaborations bring diversity to research. Diverse interactions can support critical thinking, problem-solving, and is a priority among STEM disciplines. By exploring a set of individual contributors that can be effect change through collective impact, this project will explore alternative approaches to broadly enhance diversity in STEM, such as sense of community and perceived program benefit. The S-TEAMS project relies on the use of sustainability as the organizing frame for the deployment of learning communities (teams) that engage deeply with active learning. Studies on the issue of underrepresentation often cite a feeling of isolation and lack of academically supportive networks with other students like themselves as major reasons for a disinclination to pursue education and careers in STEM, even as the numbers of underrepresented groups are increasing in colleges and universities across the country. The growth of sustainability science provides an excellent opportunity to include students from underrepresented groups in supportive teams working together on problems that require expertise in multiple disciplines. Participating students will develop professional skills and strengthen STEM- and sustainability-specific skills through real-world experience in problem solving and team science. Ultimately this project is expected to help increase the number of qualified professionals in the field of sustainability and the number of minorities in the STEM professions.
While there is certainly a clear need to improve engagement and retention of underrepresented groups across the entire spectrum of STEM education - from K-12 through graduate education, and on through career choices - the explicit focus here is on the undergraduate piece of this critical issue. This approach to teamwork makes STEM socialization integral to the active learning process. Five-member transdisciplinary teams, from disciplines such as biology, chemistry, computer and information sciences, geography, geology, mathematics, physics, and sustainability science, will work together for ten weeks in summer 2018 on real-world projects with corporations, government organizations, and nongovernment organizations. Sustainability teams with low participation by underrepresented groups will be compared to those with high representation to gather insights regarding individual and collective engagement, productivity, and ongoing interest in STEM. Such insights will be used to scale up the effort through partnership with New Jersey Higher Education Partnership for Sustainability (NJHEPS).
DATE:
-
TEAM MEMBERS:
Amy TuiningaAshwani VasishthPankaj Lai
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.
DATE:
-
TEAM MEMBERS:
Ila BermanT. Donna ChenKaren RheubanQian Cai
Becoming computationally literate is increasingly crucial to everyday life and to expanding workforce capacity. Research suggests that computational literacy--knowing what, when, how, and why to use the ideas of computer science, in combination with the capacity to view problems and potential solutions through the lens of computational structures and procedures--can be supported through digital game play. This project aims to develop a social and creative exhibit game that foregrounds aspects of computer science, specifically artificial intelligence (AI) and computer programming, in ways that enable youth to explore, construct, and share computational complex systems content with one another and other museum visitors. To play the game, pairs of youth visitors will use code cards to program the behavior of AI animals in a virtual forest. As they do so, youth will engage with computational literacy practices, such as basic computer programming, describing their computational ideas, and doing computational problem solving with their friends. Their activity will be projected on a large screen as a strategy for enabling youth to test, rehearse, and communicate their computational ideas and to also interest other visitors into computational problem solving.
Using multi-perspective and iterative design-based research, university learning scientists, museum practitioners, and game developers will pursue research questions around how science museums can better engage youth who are traditionally underrepresented in computer science in complex computational practices. Data sources will include interactive-log data, observations of visitor interactions with the game, visitor interviews, and visitor surveys. A multimodal and mixed methods approach that searches for convergences between qualitative analysis, quantitative analysis, and learning analytics will be used to generate research findings. Changes in computational literacy will be assessed by evaluating what problems visitors choose to solve with programming, how they frame those problems, and their selections from among possible solutions, what they program, how they program, and how they describe programming ideas. The results of this project will include: 1) a social, interactive gameplay experience that supports the development of computational literacy; 2) design principles for game-based exhibits that facilitate development of computational literacy; and 3) new knowledge of variations in design and gameplay across diverse gameplay users, including those from underrepresented groups in computer science. It is anticipated that 1,000 museum youth visitors will directly participate in the study.
This project 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.
DATE:
-
TEAM MEMBERS:
Matthew BerlandLeilah LyonsMatthew Cannady
This one-year Collaborative Planning project seeks to bring together an interdisciplinary planning team of informal and formal STEM educators, researchers, scientists, community, and policy experts to identify the elements, activities, and community relationships necessary to cultivate and sustain a thriving regional early childhood (ages 3-6) STEM ecosystem. Based in Southeast San Diego, planning and research will focus on understanding the needs and interests of young Latino dual language learners from low income homes, as well as identify regional assets (e.g., museums, afterschool programs, universities, schools) that could coalesce efforts to systematically increase access to developmentally appropriate informal STEM activities and resources, particularly those focused on engineering and computational thinking. This project has the potential to enhance the infrastructure of early STEM education by providing a model for the planning and development of early childhood focused coalitions around the topic of STEM learning and engagement. In addition, identifying how to bridge STEM learning experiences between home, pre-k learning environments, and formal school addresses a longstanding challenge of sustaining STEM skills as young children transition between environments. The planning process will use an iterative mixed-methods approach to develop both qualitative and quantitative and data. Specific planning strategies include the use of group facilitation techniques such as World Café, graphic recording, and live polling. Planning outcomes include: 1) a literature review on STEM ecosystems; 2) an Early Childhood STEM Community Asset Map of southeast San Diego; 3) a set of proposed design principles for identifying and creating early childhood STEM ecosystems in low income communities; and 4) a theory of action that could guide future design and research. This project is funded by the Advancing Informal STEM Learning program, which seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments.
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 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