CAREER: Enhancing Critical Reflection on Data by Integrating Users' Expectations in Visualization Interaction

Project: Research project

Project Details


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 date9/16/185/31/23


  • National Science Foundation (IIS-1930642 /003)


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