Learning Data Science Through Civic Engagement With Open Data

这个AISL试点和可行性项目将研究公众在探索和分析与他们日常生活相关的公开公民数据时所进行的数据科学学习。政府服务，如教育、交通和非紧急市政请求，正在日益数字化。一般来说，项目研讨会和活动可以支持参与者使用这些数据来回答他们自己的问题，例如：“市政府机构如何应对我附近的噪音？”和“我附近的废物和回收服务与其他社区相比如何？”本项目旨在了解如何设计和促进此类计划，以支持不同社区获取和有意义地分析数据，从而促进非正式数据科学教育中的创新和知识建设。该团队将首先总结来自各个领域的数据科学教育的最佳实践。接下来，他们将探讨纽约市的两个项目的设计和影响，纽约市是公开开放数据倡议的领导者。这一阶段将探讨活动和促进方法、参与者的目标和数据素养技能实践，并开始确定进入和参与水平的潜在障碍。最后，该团队将为其他类似组织建立能力，以探索和了解它们对社区成员参与公民数据的影响。这项试点研究将为这些项目的有效性建立初步证据，并反过来为未来的研究提供信息，以确定和扩大最佳实践，以支持公众参与数据。该研究团队将首先与学术和实践专家一起，综合基于各种文献和调查的数据科学学习最佳实践。综合结果将作为一个透镜来收集有关两个项目对参与者的数据科学实践和对公民背景下数据本质的理解的影响的初步证据。这些项目包括由市长数据分析办公室（MODA）和BetaNYC提供的项目，前者是全面负责纽约市开放数据项目的纽约市机构，后者是一家领先的非营利组织，致力于通过城市设计、技术和参与政府开放数据来改善生活。研究设计三角测量人种学观察和人工制品，前后适应的调查，以及与参与者和促进者的访谈。研究人员将确定程序性指标，并调整现有措施，以评估与公众参与数据相关的各种结果，包括：问题制定、数据集选择和操作、使用数据进行推断，以及理解可变性、抽样和背景。在社区参与公民开放数据的背景下，这些指标将通过数据科学学习的初步评估框架进行共享。研究人员还将通过文献综合、对参与者和促进者的访谈以及与我们网络中的其他组织（如纽约市社区委员会）的对话，开始确定阻碍更广泛参与的障碍。调查结果将确定所研究项目的适用性，并为未来的研究提供信息，以确定和扩大支持公众参与数据的最佳实践。该项目由美国国家科学基金会推进非正式STEM学习计划资助，该计划旨在推进非正式环境中STEM学习的设计和开发的新方法和基于证据的理解。这包括提供多种途径，扩大STEM学习经验的获取和参与，推进非正式环境中STEM学习的创新研究和评估，以及发展参与者对深度学习的理解。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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 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.