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
In partnership with the Digital NEST, students engage in near to peer learning with a technical tool for the benefit of a nonprofit that tackles issues the youth are passionate about. Youth build first from an 'internal’ Impactathon, to planning and developing an additional Impactathon for a local partner and then traveling to another partner elsewhere in the state. Participants range from 14 to 24 from UC Santa Cruz students to middle schoolers from Watsonville and Salinas.
This poster was presented at the 2019 AISL Principal Investigators Meeting.
This pilot study will examine the effectiveness of an innovative applied social change, community and technology based program on marginalized youths' access, interest, efficacy and motivation to learn and engage in digital technology applications. Using stratified near-peer and peer-to-peer mentoring approaches, the pilot builds on extant literature that indicates that peer-supported hands-on mentoring and experiences can alleviate some barriers to youth engagement in digital technologies, particularly among underrepresented groups. In this project, undergraduate students will mentor and work collaboratively with high school youth primarily of Hispanic descent and community-based organizations to develop creative technology-based solutions to address social issues and challenges within their local communities, culminating in events called Impactathons. These community-hosted local and state-wide events set this pilot project apart from similar work in the field. The Impactathons not only provide a space for intellectual discourse and problem-solving among the undergraduate-youth-community partners but the Impactathons will also codify expertise from scientists, social scientists, technologists, community leaders, and other stakeholders to develop technology-based solutions with real world application. If successful, a distal outcome will be increased youth interest in digital technologies and related fields. In the short term, favorable findings will provide preliminary evidence of success and lay the foundation for a more extensive study in the future.
This pilot project is a collaboration between the Everett Program, a student-led program for Technology and Social Change at the University of California Santa Cruz - a Hispanic Serving Institution - and the Digital NEST, a non-profit, high-tech youth career development and collaboration space for young people ages 14-24. Through this partnership and other recruitment efforts, an estimated 70-90 individuals will participate in the Impactathon pilot program over two years. Nearly two-thirds of the participants are expected to be undergraduate students. They will receive extensive training in near-peer and peer-to-peer mentoring and serve as mentors for and co-innovation developers with the high school youth participants. The undergraduates and youth will partner with local community organizations to identify a local social challenge that can be addressed through a technology-based solution. The emergent challenges will vary and could span the spectrum of STEM and applied social science topics of interest. Working in informal contexts (i.e., afterschool. weekend), the undergraduate-youth-community partner teams will work collaboratively to develop practical technology-based solutions to real world challenges. The teams will convene three times per year, locally and statewide, at student and community led Impactathons to share their work and glean insights from other teams to refine their innovations. In parallel, the research team will examine the effectiveness of the Impactathon model in increasing the undergraduate and youths' interest, motivation, excitement, engagement and learning of digital technologies. In addition to the research, the formative and summative evaluations should provide valuable insights on the effectiveness of the model and its potential for expansion and replication.
The project is co-funded by the Advancing Informal STEM Learning (AISL) Program and STEM +C. The AISL program seeks to advance new approaches to, and evidence-based understanding of, the design and development of STEM learning in informal environments. STEM + C focuses on research and development of interdisciplinary and transdisciplinary approaches to the integration of computing within STEM teaching and learning for preK-12 students in both formal and informal settings.
This 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.
This INSPIRE project addresses the issue of high volume hydraulic fracturing, also called fracking, and its effects on ground water resources. Fracking allows drillers to extract natural gas from shale deep within the earth. Methane gas sometimes escapes from shale gas wells and can contaminate water resources or leak into the atmosphere where it contributes to greenhouse gas emissions. Monitoring for these potential leaks is difficult because methane is also released into aquifers naturally, and because monitoring is time- and resource-intensive. Such subsurface leakage may also be relatively rare. This project seeks to improve overall understanding of the impacts of natural gas drilling using both advances in computer science and geoscience, and to teach the public about such impacts. The project will elucidate both the effects of human activities such as shale gas development as well as natural processes which release methane into natural waters. Results of the proposed research will lead to a better understanding of water quality in areas of shale-gas development and will highlight problems and potentially problematic management practices. The research will advance both the fields of geoscience and computer science, will train interdisciplinary graduate students, and involve citizen scientists in collecting data and understanding environmental data analysis.
The project combines new hydro-geochemical strategies and data mining approaches to study the release of methane into streams and ground waters. For example, researchers will explore how to analyze the heterogeneous spatial data that describe distributions of methane concentrations in natural waters. The objectives of this project are to i) transform the ability to measure methane in streams; ii) train citizen scientists to work with project scientists to sample streams in an area of shale-gas development and publish large-volume datasets of methane in natural waters and aquifers; iii) innovate data mining and machine learning methods for environmental data to identify anomalous spots with potential leakage; iv) run field campaigns to measure methane concentrations and isotopic signatures of water samples in these spots; v) foster dialogue among nonscientists, consultants, university scientists, members of the gas industry, government agencies, and nonprofit organizations in and beyond the target region. Toward this end, the team will host workshops aimed to build dialogue among stakeholders and will release data analytic software for environmental measurements to benefit a broader research community.
The Jackprot is a didactic slot machine simulation that illustrates how mutation rate coupled with natural selection can interact to generate highly specialized proteins. Conceptualized by Guillermo Paz-y-Miño C., Avelina Espinosa, and Chunyan Y. Bai (New England Center for the Public Understanding of Science, Roger Williams University and the University of Massachusetts, Dartmouth), the Jackprot uses simplified slot-machine probability principles to demonstrate how mutation rate coupled with natural selection suffice to explain the origin and evolution of highly specialized proteins. The