Learning Data Science Through Civic Engagement With Open Data

通过公民参与开放数据来学习数据科学

基本信息

批准号：
2005890
负责人：
Oded Nov
金额：
$ 30万
依托单位：
New York University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-10-01 至 2023-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2005890&HistoricalAwards=false
关键词：
Learning Data Science Through Civic

项目摘要

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.

这个AISL飞行员和可行性项目将研究随着公众探索和分析与他们日常生活相关的开放公民数据进行的数据科学学习。政府服务，例如教育，运输和非紧急市政要求，正变得越来越数字化。通常，计划研讨会和活动可能能够支持参与者使用此类数据来回答自己的问题，例如：“城市机构如何应对我附近的噪音？”和“我附近的浪费和回收服务与其他人相比如何？”该项目旨在了解如何设计和支持此类计划，以支持潜水员社区访问和有意义的分析数据，将促进非正式数据科学教育中的创新和知识建设。该团队将首先总结各个领域的数据科学教育中的最佳实践。接下来，他们将探索纽约市两个计划的设计和影响，这是公开可用的开放数据计划的领导者。此阶段将探索活动和设施方法，参与者的目标和数据素养技能练习，并开始确定进入和参与水平的潜在障碍。最后，团队将为其他类似组织的能力增强能力，以探索和理解他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有效性的初步证据，进而为未来的研究提供了有关识别和扩大最佳实践的研究，以支持公众与数据的参与。该研究团队将首先综合基于各种文献的数据科学学习最佳实践，并与学术和实践专家进行调查。综合结果将作为镜头应用，以收集有关两个计划对参与者数据科学实践的影响以及在公民背景下对数据性质的理解的初步证据。该计划包括市长数据分析办公室（MODA）提供的计划，该计划是纽约市公司对该城市开放数据计划的总体责任的计划，以及Betanyc是领先的非营利组织Betanyc，致力于通过公民设计，技术和参与政府开放数据，致力于改善生活。研究设计三角仪，人种学观察和工件，预先和后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化指标并调整现有措施，以评估与公众参与数据有关的各种结果，包括：问题制定，数据集选择和操纵，使用数据来提供信息以及了解可变性，采样和背景。这些指标将通过在社区参与公民开放数据的背景下通过初始评估框架来共享数据科学学习。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其他组织（例如纽约市社区委员会）进行对话来确定广播公司参与的障碍。调查结果将确定正在研究的计划的适用性，并为未来的研究提供信息，以识别和放大支持公众参与数据的最佳实践。该项目由NSF推进的非正式STEM学习计划资助，该计划旨在推进对非正式环境中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，以扩大对STEM学习经验的访问和参与，推进对非正式环境中STEM学习的创新研究和评估，并发展参与者对更深层学习的理解。该奖项反映了NSF的法定任务，并通过使用该基金会的知识分子和更广泛的影响来评估NSF的法定任务，并通过评估来诚实地进行了支持。