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.
There is a dearth of prominent STEM role models for underrepresented populations. For example, according to a 2017 survey, only 3.1% of physicists in the United States are Black, only 2.1% are Hispanic, and only 0.5% are Native American. The project will help bridge these gaps by developing exhibits that include simulations of historical scientific experiments enacted by little-known scientists of color, virtual reality encounters that immerse participants in the scientists' discovery process, and other content that allows visitors to interact with the exhibits and explore the exhibits' themes. The project will develop transportable, interactive exhibits focusing on light: how we perceive light, sources of light from light bulbs to stars, uses of real and artificial light in human endeavors, and past and current STEM innovators whose work helps us understand, create, and harness light now. The exhibits will be developed in three stages, each exploring a characteristic of light (Color, Energy, or Time). Each theme will be explored via multiple deliveries: short documentary and animated films, virtual reality experiences, interactive "photobooths," and technology-based inquiry activities. The exhibit components will be copied at seven additional sites, which will host the exhibits for their audiences, and the project's digital assets will enable other STEM learning organizations to duplicate the exhibits. The exhibits will be designed to address common gaps in understanding, among adults as well as younger learners, about light. What light really is and does, in scientific terms, is one type of hidden story these exhibits will convey to general audiences. Two other types of science stories the exhibits will tell: how contemporary research related to light, particularly in astrophysics, is unveiling the hidden stories of our universe; and hidden stories of STEM innovators, past and present, women and men, from diverse backgrounds. These stories will provide needed role models for the adolescent learners, helping them learn complex STEM content while showing them how scientific research is conducted and the diverse community of people who can contribute to STEM innovations and discoveries.
The project deliverables will be designed to present complex physics content through coherent, immersive, and embodied learning experiences that have been demonstrated to promote engagement and deeper learning. The project will research whether participants, through interacting with these exhibits, can begin to integrate discrete ideas and make connections with complex scientific content that would be difficult without technology support. For example, students and other novices often lack the expertise necessary to make distinctions between what is needed and what is extra within scientific problems. The proposed study follows a Design-Based Research (DBR) approach characterized by iterative cycles of data collection, analysis, and reflection to inform the design of educational innovations and advance educational theory. Project research includes conceiving, building, and testing iterative phases, which will enable the project to capture the complexity of learning and engagement in informal learning settings. Research participants will complete a range of research activities, including focus group interviews, observation, and pre-post assessment of science content knowledge and dispositions.
By showcasing such role models and informing about related STEM content, this project will widen perspectives of audiences in informal learning settings, particularly adolescents from groups underrepresented in STEM fields. Research findings and methodologies will be shared widely in the informal STEM learning community, building the field's knowledge of effective ways to broaden participation in informal science learning, and thus increase broaden participation in and preparation for the STEM-based workforce.
This project is funded by the National Science Foundation's (NSF's) Advancing Informal STEM Learning (AISL) program, which supports innovative research, approaches, and resources for use in a variety of learning settings.
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TEAM MEMBERS:
Todd BoyetteJill HammJanice AndersonCrystal Harden