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探索和分析与其日常生活相关的开放公民数据时发生的数据科学学习。教育、交通和非紧急市政请求等政府服务正变得越来越数字化。一般来说，计划研讨会和活动可能能够支持参与者使用这些数据来回答他们自己的问题，例如：“城市机构如何应对我家附近的噪音？“和“我的邻居的废物和回收服务与其他人相比如何？”该项目旨在了解这些计划是如何设计和促进的，以支持不同的社区访问和有意义地分析数据，这将促进非正式数据科学教育的创新和知识建设。该团队将开始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，并开始确定潜在的进入障碍和参与水平。最后，该小组将为其他类似组织建设能力，以探索和了解它们对社区成员参与公民数据的影响。这项试点研究将建立这些计划的有效性的初步证据，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。该研究团队将开始，根据各种文献和学术和实践专家的调查，综合数据科学学习的最佳实践。综合结果将作为一个透镜，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性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）提供的项目，MODA是纽约市的一个机构，全面负责城市的开放数据项目，BetaNYC是一个领先的非营利组织，致力于通过城市设计，技术和与政府开放数据的互动来改善生活。研究设计三角民族志的观察和文物，前后适应的调查，并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。这些指标将在社区参与公民开放数据的背景下，通过数据科学学习的初始评估框架进行共享。研究人员还将开始通过文献综合、与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）的对话来确定更广泛参与的障碍。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NSF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，以扩大获得和参与STEM学习经验，推进创新研究和评估的STEM学习在非正式环境中，并发展的理解，更深层次的学习参与者。该奖项反映了NSF的法定使命，并已被认为是值得通过评估使用基金会的智力价值和更广泛的影响力审查标准的支持。

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.