We characterize the factors that determine who becomes an inventor in the United States, focusing on the role of inventive ability (“nature”) vs. environment (“nurture”). Using deidentified data on 1.2 million inventors from patent records linked to tax records, we first show that children’s chances of becoming inventors vary sharply with characteristics at birth, such as their race, gender, and parents’ socioeconomic class. For example, children from high-income (top 1%) families are ten times as likely to become inventors as those from below-median income families. These gaps persist even
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TEAM MEMBERS:
Alex BellRaj ChettyXavier JaravelNeviana PetkovaJohn Van Reenen
This is a story about learning STEM content and practices while making objects. It is also a story about how that learning is contextualized in one young man’s disruption of racism simply by trying to learn how gears work. Our project, Investigating STEM Literacies in MakerSpaces (STEMLiMS), focuses on how adults and youth use representations to accomplish tasks in STEM disciplines in formal and informal making spaces (Tucker-Raymond, Gravel, Kohberger, & Browne, 2017). Making is an interdisciplinary endeavor that may involve mechanical and electrical engineering, digital literacies and
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
AHA! Island is a new project that uses animation, live-action videos, and hands-on activities to support joint engagement of children and caregivers around computational thinking concepts and practices. This research is intended to examine the extent to which the prototyped media and activity sets support the project’s learning goals. Education Development Center (EDC), WGBH’s research partner for the project, conducted a small formative study with 16 English-speaking families (children and their caregivers) to test out these media and activity set prototypes. During the in-person video
The University of Texas at Austin's Texas Advanced Computing Center, Chaminade University of Honolulu (CUH), and the Georgia Institute of Technology will lead this NSF INCLUDES Design and Development Launch Pilot (DDLP) to establish a model for data science preparation of Native Hawaiian and Pacific Islander (NHPI) students at the high school and undergraduate levels. The project is premised on the promise of NHPI communities gaining access to, and the ability to work with, large data sets to tackle emerging problems in the Pacific. Such agency over "big data" sets that are relevant to Pacific issues, and contemporary skills in data science, analytics and visualization have the potential to be transformative for community improvement efforts. The effort has the potential to advance knowledge, instructional pedagogy and practices to improve NHPI high school and undergraduate students performance in and attraction to STEM education and careers.
The project team will work to: 1) Increase interest and proficiency in data science and visualization among NHPI high school and undergraduate students through a summer immersion experience that bridges computation and culture; 2) Build data science capacity at an NHPI serving undergraduate institution (CUH) through creation of a certificate program; and 3) Develop and expand partnerships with other organizations with related goals working with NHPI populations. The month-long summer training for 20 NHPI college students, and five NHPI high school students, takes place at CUH and focuses on data science, visualization, and virtual reality, including working on problem sets that require data science approaches and incorporate geographically, socially- and culturally-relevant research themes.
The University of New Hampshire (UNH) NSF INCLUDES Design and Development Launch Pilot project is a collaborative effort with the Community College System of New Hampshire, Advanced Manufacturing (AM) businesses, NH Economic Development, and the University of New Hampshire to address workforce development in the Advanced Manufacturing sector in the state. The Advanced Manufacturing Program (AMP) uses a framework built on the Collective Impact collaboration model that enables AMP partners to innovate, plan, and implement strategies that significantly increase NH's community colleges (CC) as a source for future workers and leaders in AM.
Specifically, this proposal addresses the pressing need for increasing numbers of AM workers through strategies designed to increase the retention of low socioeconomic status (LSES) students in CC STEM degree programs. AMP coordinates four key implementation strategies: 1) Co-requisite remediation within mathematics and quantitative reasoning; 2) Guided Pathways mentorship with "high touch" advising and student guidance resources that combines clearly defined academic pathways leading to 4-year college transfer and job placement; 3) paid work-based learning (WBL) experiences in industry and academic research; and 4) mentor inclusiveness training to prepare the workplace and academic settings to receive LSES students into a supportive climate. Successfully coordinating these four components through the process of Collective Impact collaboration will lead to a flexible and integrated AM workforce pipeline that serves CC AM students, AM industry partners, and the state as a whole. Findings will be disseminated to academic, business, and government stakeholders in NH, the region, and nationally to inform and improve broadening participation initiatives.
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TEAM MEMBERS:
Palligarnai VasudevanStephen HaleBrad KinseyLeslie BarberMelissa Aikens
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).
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TEAM MEMBERS:
Amy TuiningaAshwani VasishthPankaj Lai
resourceprojectProfessional Development, Conferences, and Networks
The American Association for the Advancement of Science (AAAS) and the National Science Foundation (NSF) will continue its collaboration in providing to early- and mid-career scientists and engineers experiential professional development and public service fellowships via the AAAS Science and Technology Fellowship Program. Consistent with the immersion model adopted by AAAS, Fellows at NSF will be selected annually through a competitive process and placed in organizations throughout the Foundation. Fellows will work with NSF staff on a broad range of activities in order to gain insight into how national science and technology policy goals are translated into and reflected by NSF's mission and strategic goals and how and by whom national science and technology policy is driven, shaped and prioritized. NSF fellowship assignments are designed to: educate and expose Fellows to NSF programmatic planning, development and oversight activities in all fields of fundamental research via hands-on engagement; utilize the Fellows' expertise on projects that apprise NSF officials in areas of mutual interest to the Fellow and the host organization; and provide developmental opportunities to inform future career decisions. The program includes an orientation on executive branch and congressional operations, as well as a year-long suite of knowledge- and skill-building seminars involving science, technology and public policy within the federal as well as NSF contexts.
In the long-term, the AAAS Fellowship program seeks to build leadership capacity for a strong national science and engineering enterprise. Upon completion of the Fellowship, Fellows will have gained: a broader understanding and increased insights about the development and execution of federal-level science, technology, engineering and mathematics policies and initiatives as well as how policy and science intersect; enhanced skills in communicating science to support policy development; and a greater capacity to serve more effectively in future leadership roles in diverse environments, including public and policy arenas, academia and the private sector. The ultimate outcome of the Fellowship program experience -- policy savvy science and engineer leaders who understand government and policymaking and are well-trained to develop and execute solutions to address the nation's challenges.
The Montana Girls STEM Collaborative brings together organizations and individuals throughout Montana who are committed to informing and motivating girls to pursue careers in STEM – Science, Technology, Engineering and Mathematics. The Collaborative offers professional development, networking and collaboration opportunities to adults who offer and/or support STEM programs for girls and other youth typically under-represented in STEM. The vision of Montana Girls STEM is that every young person in Montana has the opportunity to learn about STEM careers and feels welcome pursuing any dream they
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TEAM MEMBERS:
Suzi TaylorRay CallawayCathy Witlock
As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public's engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner's speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The 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. The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry's (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy - currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.