Data Visualization Literacy: Research and Tools that Advance Public Understanding of Scientific Data

数据可视化素养：促进公众对科学数据理解的研究和工具

• 批准号: 1713567
• 负责人: Katy Borner
• 金额: $ 135.52万
• 依托单位: Indiana University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1713567&HistoricalAwards=false
• 关键词: Data; Visualization; Literacy; Research; Tools

As the world is increasingly dependent upon computing and computational processes associated with data analysis, it is essential to gain a better understanding of the visualization technologies that are used to make meaning of massive scientific data. It is also essential that the infrastructure, the very means by which technologies are developed for improving the public's engagement in science itself, be better understood. Thus, this AISL Innovations in Development project will address the critical need for the public to learn how to interpret and understand highly complex and visualized scientific data. The project will design, develop and study a new technology platform, xMacroscope, as a learning tool that will allow visitors at the Science Museum of Minnesota and the Center of Science and Industry, to create, view, understand, and interact with different data sets using diverse visualization types. The xMacroscope will support rapid research prototyping of public experiences at selected exhibits, such as collecting data on a runner's speed and height and the visualized representation of such data. The xMacroscope will provide research opportunities for exhibit designers, education researchers, and learning scientists to study diverse audiences at science centers in order to understand how learning about data through the xMacroscope tool may inform definitions of data literacy. The research will advance the state of the art in visualization technology, which will have broad implications for teaching and learning of scientific data in both informal and formal learning environments. The project will lead to better understanding by science centers on how to present data to the public more effectively through visualizations that are based upon massive amounts of data. Technology results and research findings will be disseminated broadly through professional publications and presentations at science, education, and technology conferences. The 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.The project is driven by the assumption that in the digital information age, being able to create and interpret data visualizations is an important literacy for the public. The research will seek to define, measure, and advance data visualization literacy. The project will engage the public in using the xMacrocope at the Science Museum of Minnesota and at the Center of Science and Industry's (COSI) science museum and research center in Columbus, Ohio. In both museum settings the public will interact with different datasets and diverse types of visualizations. Using the xMacroscope platform, personal attributes and capabilities will be measured and personalized data visualizations will be constructed. Existing theories of learning (constructivist and constructionist) will be extended to capture the learning and use of data visualization literacy. In addition, the project team will conduct a meta-review related to different types of literacy and will produce a definition with performance measures to assess data visualization literacy - currently broadly defined in the project as the ability to read, understand, and create data visualizations. The research has potential for significant impact in the field of science and technology education and education research on visual learning. It will further our understanding of the nature of data visualization literacy learning and define opportunities for visualizing data in ways that are both personally and culturally meaningful. The project expects to advance the understanding of the role of personalization in the learning process using iterative design-based research methodologies to advance both theory and practice in informal learning settings. An iterative design process will be applied for addressing the research questions by correlating visualizations to individual actions and contributions, exploring meaning-making studies of visualization construction, and testing the xMacroscope under various conditions of crowdedness and busyness in a museum context. The evaluation plan is based upon a logic model and the evaluation will iteratively inform the direction, process, and productivity of the project.

随着世界越来越依赖与数据分析相关的计算和计算过程，更好地理解用于理解海量科学数据的可视化技术至关重要。同样重要的是，更好地理解基础设施，即开发技术以提高公众对科学本身的参与度的手段。因此，AISL 创新发展项目将满足公众学习如何解释和理解高度复杂和可视化的科学数据的迫切需求。该项目将设计、开发和研究一个新技术平台 xMacrscope，作为一种学习工具，让明尼苏达科学博物馆和科学与工业中心的参观者能够创建、查看、理解不同的数据集并与之交互使用不同的可视化类型。 xMacrscope 将支持在选定的展览中快速研究公众体验的原型，例如收集跑步者的速度和高度的数据以及这些数据的可视化表示。 xMacrscope 将为展览设计师、教育研究人员和学习科学家提供研究机会，以研究科学中心的不同受众，以便了解通过 xMacrscope 工具学习数据如何为数据素养的定义提供信息。该研究将推动可视化技术的发展，这将对非正式和正式学习环境中的科学数据教学产生广泛影响。该项目将使科学中心更好地了解如何通过基于大量数据的可视化更有效地向公众呈现数据。技术成果和研究成果将通过专业出版物和科学、教育和技术会议上的演讲广泛传播。该项目由推进非正式 STEM 学习 (AISL) 计划资助，该计划旨在推进非正式环境中 STEM 学习设计和开发的新方法以及基于证据的理解。这包括提供多种途径来扩大 STEM 学习体验的获取和参与，推进非正式环境中 STEM 学习的创新研究和评估，以及加深参与者对更深层次学习的理解。该项目的驱动因素是：在数字信息中当今时代，能够创建和解释数据可视化是公众的重要素养。该研究将寻求定义、衡量和提高数据可视化素养。该项目将让公众在明尼苏达州科学博物馆以及俄亥俄州哥伦布市科学与工业中心 (COSI) 科学博物馆和研究中心使用 xMacrocope。在这两个博物馆环境中，公众将与不同的数据集和不同类型的可视化进行交互。使用xMacrscope平台，将测量个人属性和能力，并构建个性化数据可视化。现有的学习理论（建构主义和建构主义）将被扩展以捕获数据可视化素养的学习和使用。此外，项目团队将进行与不同类型素养相关的元审查，并将制定一个包含绩效衡量指标的定义，以评估数据可视化素养——目前项目中广泛定义为阅读、理解和创建数据可视化的能力。该研究在科技教育领域和视觉学习的教育研究领域具有潜在的重大影响。它将进一步加深我们对数据可视化素养学习本质的理解，并定义以对个人和文化有意义的方式可视化数据的机会。该项目期望使用基于迭代设计的研究方法来促进对学习过程中个性化作用的理解，以推进非正式学习环境中的理论和实践。将采用迭代设计过程来解决研究问题，将可视化与个人行为和贡献相关联，探索可视化构建的意义研究，并在博物馆环境中各种拥挤和繁忙的条件下测试 xMacrscope。评估计划基于逻辑模型，评估将迭代地告知项目的方向、过程和生产力。

项目成果

Visualizing learner engagement, performance, and trajectories to evaluate and optimize online course design

可视化学习者的参与度、表现和轨迹，以评估和优化在线课程设计

• DOI: 10.1371/journal.pone.0215964
• 发表时间: 2019-05
• 期刊: PLOS ONE
• 影响因子: 3.7
• 作者: Ginda, Michael;Richey, Michael C.;Cousino, Mark;Börner, Katy;Dalby, Andrew R.
• 通讯作者: Dalby, Andrew R.

Mapping the co-evolution of artificial intelligence, robotics, and the internet of things over 20 years (1998-2017)

绘制 20 年来人工智能、机器人和物联网的共同演化图（1998-2017）

• DOI: 10.1371/journal.pone.0242984
• 发表时间: 2020
• 期刊: PLoS ONE
• 影响因子: 3.7
• 作者: Börner K;Scrivner O;Cross LE;Gallant M;Ma S;Martin AS;Record L;Yang H;Dilger JM
• 通讯作者: Dilger JM

Visualizing big science projects

可视化大型科学项目

• DOI: 10.1038/s42254-021-00374-7
• 发表时间: 2021-11
• 期刊: Nature Reviews Physics
• 影响因子: 38.5
• 作者: Börner, Katy;Silva, Filipi Nascimento;Milojević, Staša
• 通讯作者: Milojević, Staša

Skill discrepancies between research, education, and jobs reveal the critical need to supply soft skills for the data economy

研究、教育和工作之间的技能差异揭示了为数据经济提供软技能的迫切需要

• DOI: 10.1073/pnas.1804247115
• 发表时间: 2018-12-10
• 期刊: Proceedings of the National Academy of Sciences
• 作者: K. Börner;O. Scrivner;Mike Gallant;Shu;Xiaozhong Liu;Keith Chewning;Lingfei Wu;James A. Evans
• 通讯作者: James A. Evans

Toward a more scientific science

迈向更科学的科学

• DOI: 10.1126/science.aav2484
• 发表时间: 2018-09-21
• 期刊: Science
• 影响因子: 56.9
• 作者: Pierre Azoulay;J. Graff;Brian Uzzi;Dashun Wang;Heidi L. Williams;James A. Evans;G. Jin;S. F. Lu;Benjamin F. Jones;K. Börner;K. Lakhani;K. Boudreau;E. Guinan
• 通讯作者: E. Guinan