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.
Numeracy is not a luxury: numbers constantly factor into our daily lives. Yet adults in the United States have lower numeracy than adults in most other developed nations. While formal statistical training is effective, few adults receive it – and schools are a major contributor to the inequity we see among U.S. adults. That leaves news well-poised as a source of informal learning, given that news is a domain where adults regularly encounter quantitative content. Our transdisciplinary team of journalists and social scientists propose a research agenda for thinking about math and the news. We
Many studies have examined the impression that the general public has of science and how this can prevent girls from choosing science fields. Using an online questionnaire, we investigated whether the public perception of several academic fields was gender-biased in Japan. First, we found the gender-bias gap in public perceptions was largest in nursing and mechanical engineering. Second, people who have a low level of egalitarian attitudes toward gender roles perceived that nursing was suitable for women. Third, people who have a low level of egalitarian attitudes perceived that many STEM
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TEAM MEMBERS:
Yuko IkkataiAzusa MinamizakiKei KanoAtsushi InoueEuan McKayHiromi M. Yokoyama
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