Informal STEM learning experiences (ISLEs), such as participating in science, computing, and engineering clubs and camps, have been associated with the development of youth’s science, technology, engineering, and mathematics interests and career aspirations. However, research on ISLEs predominantly focuses on institutional settings such as museums and science centers, which are often discursively inaccessible to youth who identify with minoritized demographic groups. Using latent class analysis, we identify five general profiles (i.e., classes) of childhood participation in ISLEs from data
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TEAM MEMBERS:
Remy DouHeidi CianZahra HazariPhilip SadlerGerhard Sonnert
New York City is a leader in Open Data initiatives, and has a large and diverse population. This project studies informal data science learning at workshops and trainings associated with NYC’s open data ecosystem.
This poster was presented at the 2021 NSF AISL Awardee Meeting.
Many Black youth in both urban and rural areas lack engaging opportunities to learn mathematics in a manner that leads to full participation in STEM. The Young People’s Project (YPP), the Baltimore Algebra Project (BAP), and the Education for Liberation Network (EdLib) each have over two decades of experience working on this issue. In the city of Baltimore, where 90% of youth in poverty are Black, and only 5% of these students meet or exceed expectations in math, BAP, a youth led organization, develops and employs high school and college age youth to provide after-school tutoring in Algebra 1, and to advocate for a more just education for themselves and their peers. YPP works in urban or rural low income communities that span the country developing Math Literacy Worker programs that employ young people ages 14-22 to create spaces to help their younger peers learn math. Building on these deep and rich experiences, this Innovations in Development project studies how Black students see themselves as mathematicians in the context of paid peer-to-peer math teaching--a combined social, pedagogical, and economic strategy. Focusing primarily in Baltimore, the project studies how young people grow into new self-definitions through their work in informal, student-determined math learning spaces, structured collaboratively with adults who are experts in both mathematics and youth development. The project seeks to demonstrate the benefits of investing in young people as learners, teachers, and educational collaborators as part of a core strategy to improve math learning outcomes for all students.
The project uses a mixed methods approach to describe how mathematical identity develops over time in young people employed in a Youth-Directed Mathematics Collaboratory. 60 high school aged students with varying mathematical backgrounds (first in Baltimore and later in Boston) will learn how to develop peer- and near-peer led math activities with local young people in informal settings, after-school programs, camps, and community centers, reaching approximately 600 youth/children. The high school aged youth employed in this project will develop their own math skills and their own pedagogical skills through the already existing YPP and BAP structures, made up largely of peers and near-peers just like themselves. They will also participate in on-going conversations within the Collaboratory and with the community about the cultural significance of doing mathematics, which for YPP and BAP is a part of the ongoing Civil Rights/Human Rights movement. Mathematical identity will be studied along four dimensions: (a) students’ sequencing and interpretation of past mathematical experiences (autobiographical identity); (b) other people’s talk to them and their talk about themselves as learners, doers, and teachers of mathematics (discoursal identity); (c) the development of their own voices in descriptions and uses of mathematical knowledge and ideas (authorial identity); and (d) their acceptance or rejection of available selfhoods (socio-culturally available identity). Intended outcomes from the project include a clear description of how mathematical identity develops in paid peer-teaching contexts, and growing recognition from both local communities and policy-makers that young people have a key role to play, not only as learners, but also as teachers and as co-researchers of mathematics education.
This Innovations in Development project is funded by the Advancing Informal STEM Learning (AISL) program.
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TEAM MEMBERS:
Jay GillenMaisha MosesThomas NikundiweNaama LewisAlice Cook
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 AISL Pilots and Feasibility project will study the data science learning that takes place as members of the public explore and analyze open civic data related to their everyday lives. Government services, such as education, transportation, and non-emergency municipal requests, are becoming increasingly digital. Generally, program workshops and events may be able to support participants in using such data to answer their own questions, such as: "How do City agencies respond to noise in my neighborhood?" and "How do waste and recycling services in my neighborhood compare with others?" This project seeks to understanding how such programs are designed and facilitated to support diverse communities in accessing and meaningfully analyzing data will promote innovation and knowledge building in informal data science education. The team will begin by summarizing best practices in data science education from a variety of fields. Next they will explore the design and impacts of two programs in New York City, a leader in publicly available Open Data initiatives. This phase will explore activities and facilitation approaches, participants' objectives and data literacy skills practice, and begin to identify potential barriers to entry and levels of participation. Finally, the team will build capacity for other similar organizations to explore and understand their impacts on community members' engagement with civic data. This pilot study will establish preliminary evidence of the effectiveness of these programs, and in turn, inform future research into the identifying and amplifying best practices to support public engagement with data.
This research team will begin by synthesizing data science learning best practices based on varied literatures and surveys with academic and practitioner experts.
Synthesis results will be applied as a lens to gather preliminary evidence regarding the impacts of two programs on participants' data science practices and understanding of the nature of data in the context of civics. The programs include one offered by the Mayor's Office of Data Analytics (MODA), which is the NYC agency with overall responsibility for the City's Open Data programs, and BetaNYC, a leading nonprofit organization working to improve lives through civic design, technology, and engagement with government open data. The research design triangulates ethnographic observations and artifacts, pre and post adapted surveys, and interviews with participants and facilitators. Researchers will identify programmatic metrics and adapts existing measures to assess various outcomes related to public engagement with data, including: question formulation, data set selection and manipulation, the use of data to make inferences, and understanding variability, sampling and context. These metrics will be shared through an initial assessment framework for data science learning in the context of community engagement with civic open data. Researchers will also begin to identify barriers to broader participation through literature synthesis, interviews with participants and facilitators, and conversations with other organizations in our networks, such as NYC Community Boards. Findings will determine the suitability of the programs under study and inform future research to identify and amplify best practices in supporting public engagement with data.
This project is funded by the NSF 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. 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 Pilots and Feasibility Studies 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.