Improving Graph Literacy and Numeracy

该奖项是作为NSF的社会，行为和经济科学博士后研究奖学金（SPRF）计划的一部分提供的，并得到SBE的感知，行动和认知计划的支持。SPRF计划的目标是为学术界，工业或私营部门和政府的科学事业准备有前途的早期职业博士级科学家。SPRF的奖励包括在知名科学家的赞助下进行两年的培训，并鼓励博士后研究员进行独立研究。NSF致力于促进来自科学界各部门的科学家，包括来自代表性不足的群体的科学家参与其研究计划和活动;博士后期间被认为是实现这一目标的专业发展的重要水平。每个博士后研究员必须解决推进各自学科领域的重要科学问题。在西北大学Steven Franconeri博士的赞助下，这个博士后奖学金支持一位早期职业科学家研究帮助人们理解数据可视化的方法。准确解释数据的可视化对于科学、技术、工程和数学（STEM）领域的成功至关重要。科学家依靠图形来交流和发展关于数据的想法。此外，人们使用数据的可视化来做出大规模的政策决策，例如在飓风发生之前将资源分配到哪里，以及更多的个人决策，例如接受何种医疗。鉴于可视化的广泛使用及其全球影响，每个人都能有效地使用数据可视化是很重要的。不幸的是，并非所有人都能轻松理解数据的可视化。三分之一的美国人口表现出较低的图形读写能力和算术能力，或者是不发达的能力与图形呈现的信息和数字。图形素养和计算能力是有助于可视化素养的两个关键因素。这项工作通过确定低可视化素养的原因，并开发免费和公平的资源来提高可视化素养，为STEM中的弱势学生消除障碍。这个项目的重点是确定认知因素的性质，产生困难的推理与可视化。在最近的一篇综述论文中，帕迪利亚及其同事提出了一个认知模型，用于研究人们如何通过可视化做出决策（帕迪利亚，Creem-Regehr，Hegarty，Stefanucci，2018）。 在此模型的基础上，目前的项目旨在确定认知组件，导致低可视化素养的人对可视化的误解。这些研究提供了关于不同人群如何理解和使用数据可视化的新知识，此外还为如何帮助人们通过数据可视化做出最佳决策提供了实用建议。这项工作的结果加强了我们对难以理解STEM图形的人的可视化认知的基本理解。该项目还为教育工作者提供了经验验证的方法，帮助视觉化素养低的学生使用和理解数据的视觉化，从而消除了这些弱势群体在STEM中取得成功的障碍。该奖项反映了NSF的法定使命，并通过使用基金会的智力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 award was provided as part of NSF's Social, Behavioral and Economic Sciences Postdoctoral Research Fellowships (SPRF) program and is supported by SBE's Perception, Action and Cognition program. The goal of the SPRF program is to prepare promising, early career doctoral-level scientists for scientific careers in academia, industry or private sector, and government. SPRF awards involve two years of training under the sponsorship of established scientists and encourage Postdoctoral Fellows to perform independent research. NSF seeks to promote the participation of scientists from all segments of the scientific community, including those from underrepresented groups, in its research programs and activities; the postdoctoral period is considered to be an important level of professional development in attaining this goal. Each Postdoctoral Fellow must address important scientific questions that advance their respective disciplinary fields. Under the sponsorship of Dr. Steven Franconeri at Northwestern University, this postdoctoral fellowship award supports an early career scientist investigating ways to help people understand visualizations of data. Accurately interpreting visualizations of data is vital for success in science, technology, engineering, and math (STEM) fields. Scientists depend on graphics to communicate and develop ideas about data. Further, people use visualizations of data to make large-scale policy decisions, such as where to allocate resources before a hurricane and more personal decisions, such as which medical treatment to undergo. Given the widespread use of visualizations and their global impact, it is important that everyone can use visualizations of data effectively. Unfortunately, not all people can understand visualizations of data easily. One-third of the US population exhibits low graph literacy and numeracy, or the underdeveloped ability to work with graphically presented information and numbers. Graph literacy and numeracy are two key factors that contribute to visualization literacy. This work removes barriers for disadvantaged students in STEM by identifying the causes of low visualization literacy, and developing free and equitable resources to improve visualization literacy. This project focuses on determining the nature of the cognitive factors that produce difficulty in reasoning with visualizations. In a recent review paper, Padilla and colleagues proposed a cognitive model for how people make decisions with visualizations (Padilla, Creem-Regehr, Hegarty, & Stefanucci, 2018). Building on this model, the current project seeks to identify cognitive components that lead to a misunderstanding of visualizations for people with low visualization literacy. These studies contribute new knowledge about how diverse groups of people understand and use data visualizations, in addition to providing practical recommendations for how to help people make their best possible decisions with visualizations of data. The results of this work strengthen our basic understanding of visualization cognition for people that have difficulty understanding STEM graphics. This project also provides educators with empirically tested methods for helping students with low visualization literacy use and understand visualizations of data, thusly removing a barrier to success in STEM for these disadvantaged populations.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.