Air Pollution Visualizations for Promoting Data Literacy with Middle Schoolers and the Public

空气污染是一个数据丰富的问题空间，庞大的传感器网络不断捕获和报告测量结果。许多人对测量的污染物表面上很熟悉，但很少有人了解任何单个污染物的具体来源，可接受的水平和负面影响。此外，正规学校课程中关于空气质量的内容也很有限。该项目旨在通过公共信息站加深公众对空气质量数据的理解，并通过青年夏令营培养中学生对空气质量数据的理解。该项目将研究如何设计空气质量数据交互（通过这两个并行设计：公共信息亭和青年夏令营），以支持学习者在数据调查，可视化和通信方面的个人代理，以及这些经验如何帮助非专家了解他们的环境。该项目以超越空气质量指数（AQI）简化指标的方式扩大了公众对空气质量数据的参与。由于这项工作有可能缩小科学界和公众之间的知识差距，因此它可能是在减轻空气污染方面实施有意义变革的重要起点。这项为期四年的跨学科努力借鉴了学习科学，可视化，初中和高中STEM教育，和教师学习和专业发展，为非正式学习创造两个设计空间，旨在促进科学数据推理的参与和自我效能。该项目将通过空气质量可视化来推进人与数据交互（HDI）设计的知识，并支持环境数据素养。数据可视化和环境（DVE）夏令营将与当地中学科学教育工作者合作设计，并将邀请亚特兰大地区的中学生到格鲁吉亚理工学院校园参加为期2周的赞助计划，在此期间，他们将与科学教育工作者和可视化专家合作收集，可视化和显示环境数据。这些资助营地的招聘将优先考虑科学领域服务不足和代表性不足的群体。通过首先使用空气质量数据，然后使用他们感兴趣的自选环境数据集，学生将参与生成和提出有关当地环境的循证论据的科学实践，以调查与他们自己的社区，文化和兴趣相关的项目。该项目将同时开发和维护情境化空气质量时间线（ContAQT）平台，作为亚特兰大市中心的公共数据显示，分解当前和历史的当地多污染物空气质量数据。该项目将回答以下研究问题：（1）非正式环境中的数据交互如何支持对数据解释和环境的学习？(2)如何设计数据交互来支持学习者研究个人兴趣问题的机会？以及（3）哪些设计特征支持非专家学习者的数据探索？通过基于设计的研究，该团队将调查界面和交互的变化如何影响数据流畅性指标，如统计推理谈话（识别离群值，对趋势的推理等），与先前知识的联系，并提出问题。使用定性研究方法，如猜想映射，该项目将调查如何在简短的数据交互，自由选择的公共信息亭交互（ContAQT）以及扩展的数据调查（夏令营）可以帮助学习者提出问题，使连接到现有的知识，并提出和评估索赔。这项工作将推进以下领域的知识：1）构建科学数据的最佳实践，以便于外行观众访问; 2）了解可视化界面和交互中的具体设计决策如何影响数据交互和学习对话; 3）设计夏令营活动，以支持中学生发展数据流畅性和提高科学自我效能; 4）综合科学教育工作者的最佳实践中的知识，让女性，黑人和/或拉丁裔学习者参与数据实践。该项目由推进非正式STEM学习（AISL）计划资助，该计划旨在推进新的方法，以及对非正式环境中STEM学习的设计和发展的循证理解。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。

Air pollution is a data-rich problem space, with vast networks of sensors constantly capturing and reporting measurements. The measured pollutants are superficially familiar to many people, but few understand the specific sources, acceptable levels, and negative effects of any individual pollutant. In addition, coverage of air quality in formal school curricula is limited. This project seeks to deepen the public's understanding of air quality data through a public kiosk and to develop middle schoolers' understanding of air quality data through a youth summer camp. The project will investigate how air quality data interactions (via these two concurrent designs: the public kiosk and the youth summer camp) can be designed to support learners' personal agency in data investigations, visualizations, and communications as well as how these experiences help non-experts learn about their environment. This project expands public engagement with air quality data in ways that extend beyond the simplified metric of the Air Quality Index (AQI). Because this work has the potential to lessen the knowledge gap between the scientific community and the general public it could be an important starting point for enacting meaningful change in mitigating air pollution.This four-year interdisciplinary effort draws on expertise in the learning sciences, visualization, middle and high school STEM education, and teacher learning and professional development to create two design spaces for informal learning aimed toward advancing engagement and self-efficacy in reasoning with scientific data. This project will advance knowledge in human-data interaction (HDI) design and support environmental data literacy through air quality visualizations. The Data Visualization and the Environment (DVE) summer camp will be designed in collaboration with local middle school science educators and will invite middle school students from the Atlanta area to Georgia Tech's campus for a sponsored 2-week program during which they will engage with science educators and visualization experts to collect, visualize, and display environmental data. Recruitment for these funded camps will prioritize underserved and underrepresented groups in science. By working first with air quality data and then a self-selected environmental dataset of interest to them, students will participate in the scientific practices of generating and presenting evidence-based arguments about their local environment to investigate projects of relevance to their own community, culture, and interests. The project will concurrently develop and iterate the Contextualized Air Quality Timelines (ContAQT) platform as a public data display in Midtown Atlanta breaking down current and historical local multipollutant air quality data. The project will answer the following research questions: (1) How can data interactions in informal settings support learning about data interpretations and the environment? (2) How can data interactions be designed to support learners' opportunities to investigate questions of personal interest? and (3) What design features support data exploration by non-expert learners? Through design-based research the team will investigate how changes to the interface and interaction impact data fluency indicators such as statistical reasoning talk (identifying outliers, reasoning about trends, etc.), connections to prior knowledge, and asking questions. Using qualitative research methods, such as conjecture mapping, the project will investigate how data interactions in brief, free-choice public kiosk interactions (ContAQT) as well as extended data investigations (summer camp) can assist learners in asking questions, making connections to existing knowledge, and proposing and evaluating claims. This work will advance knowledge in the following areas: 1) best practices for framing scientific data to be accessible to lay audiences; 2) understanding of how specific design decisions in visualization interfaces and interactions can influence data interactions and learning talk; 3) design of summer camp activities to support middle school students in developing data fluency and increasing self-efficacy in science; 4) synthesizing knowledge from science educators in best practices for engaging female, Black, and/or Latine learners in data practices.This project is funded by the Advancing Informal STEM Learning (AISL) 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.