Overview: Being able to critically assess data-based claims is essential for informed decision making. Information visualizations are an engaging format for novices to use to deliberate on data that pervades news, data reports, scientific articles, and other sources. When the reader of a data-based article looks at a graph or chart, their prior knowledge and expectations about what the data means or should look like (e.g., I thought unemployment was down this year)influence their interpretation. When a reader aligns these internal representations with the visualized data, for example by noting where the data confirms or contradicts their expectations,they can more easily think critically about the data and update gaps in their knowledge. Unfortunately, most interactive information visualizations do not enable users to articulate or interact with their expectations about data. Consequently, users develop superficial understandings of data, failing to recognize and update their prior knowledge based on the presented data. The proposed work will design and evaluate a new paradigm of information visualization tools in which interaction with one's prior knowledge and expectations around data are integral. The PI will develop and study novel expectation visualization (EV) tools: interfaces that allow viewers to graphically articulate, interact with, and see feedback on their predictions about data. By integrating expectations with base data in a visualization, EV provides opportunities for users to recognize and repair gaps in their knowledge. The PI's preliminary results indicate that data is more memorable when EV is applied. The proposed work will delimit which data interpretation contexts-including which potential users in which situations' are most promising for applying EV, in addition to providing a broad set of applications and knowledge to stimulate adoption and further study. Specifically, the PI will 1) conduct controlled studies to determine the impact of underlying mechanisms of EV on data memorability, beliefs, and motivation to interact among users with varying levels of prior knowledge; 2) build a design space and demonstrative platform for sharing EV code and examples and gathering data journalists', visualization researchers', and professional visualization designers' impressions of EV's utility in various visualization contexts; and 3) invent and study EV applications that apply EV to account for prior expectations in visual analysis, improve reasoning about uncertainty among non-statisticians and researchers, engage news readers to more critically assess reported scientific studies, and gather and communicate expert opinions on data. These research activities are closely aligned with the PI's educational goal, which is to apply EV to foster data and statistical literacy in informatics and data science curricula. To achieve this, the PI will 1) develop and deploy active learning modules that integrate EV as a core competency in introductory Informatics courses, 2) develop a new "Thinking with Data" course that applies EV to enhance critical thinking in data science curricula, and 3) introduce EV to applied scientists through Data Science events and to visualization and HCI experts through international conference events. Intellectual Merit: 1) Empirical findings on how predicting data, receiving personalized feedback on predictions, and selfexplaining gaps between predictions and data enhance data memorability, posterior beliefs, and other outcomes; 2) a taxonomy characterizing key desi
|Effective start/end date||9/16/18 → 5/31/23|
- National Science Foundation (IIS-1930642)
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