Improving Graph Literacy and Numeracy

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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.