Going Beyond Notice and Wonder: Reading Data Visualizations

Anita Sundrani and Travis Weiland

Acknowledgement

This material is based upon work supported by the National Science Foundation under DRK-12 Grant No. 2143816.

Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.�

Graphs We See in Math Class

Graphs We See Outside of Math Class

Rethinking Reading Data Visualization

• We need to update what data visualizations students have opportunities to make sense of in their mathematics class
• Which means we need to update how we thinking about teaching about reading data visualizations

Launch

• What do you notice?
• What do you wonder?
• How does this impact your community? 

Reading Data Framework

• In the media
• Evolution of data visualizations
• Constructing w/ fully understanding
• Different levels of reading
• Reading the data
• Reading between the data
• Reading beyond the data
• Reading behind the data

Reading the Data

• Locating and extracting relevant information from data visualization
• Involves
• Recognizing the components of data visualizations, the interrelationships among these components, and the effect of these components on the presentation of information in data visualizations
• Speaking the language of specific data visualizations when reasoning about information displayed in graphical form
• Looking for oneself in the data

Reading the Data

• What do you notice?
• What data are displayed in this visualization?
• How does this data visualization make you feel?

Reading Between the Data

• Find patterns or relationships in the data visualization
• Involves
• Understanding relationships among tables, graphs, and data
• Making sense of a graph, but avoiding personalization and maintaining an objective stance while talking about the graphs
• Recognizing the types of relationships (correlational or causal) can be claimed based on the data collection methods
• Making sense of the data visualization in relation to your personal experiences
• Identifying and questioning how the author has highlighted particular relationships in the graph
• Reimagining other ways that relationships could be highlighted or visualized

Reading Between the Data

• What relationship(s) is the author highlighting?
• What is the relationship between the proportion of the Black population in DC and farmers markets & grocery stores?
• How do the relationships displayed here compare to your own experiences?

Reading Beyond the Data

• Move beyond the data visualization to making predictions or inferences, answering a question
• Involves
• Interpreting information in a graph and answering questions about it
• Predicting outcomes based on reasonable claims made from the graph
• Recognition of One’s Own Sociopolitical/ Critical Consciousness
• Acknowledging Alternate Explanations
• Recognizing the story, the author is trying to tell with this data
• Questioning the author’s motives for telling this story
• Identifying the inequities in the interpretation of the data visualization
• Understanding one’s social location, subjectivity, political context and having a sociohistorical and political knowledge of self and understanding how it influences one’s interpretation of information.

Reading Beyond the Data

• What do you think the purpose of this data visualization is?
• What assumptions are the authors making with this data visualization?

Reading Behind the Data

• Making connections between the context and the data visualization including how the context of how that data was collected and represented and how those aspects shape our view of the context
• Involves
• Looking for possible causes of variation based on the context being measured and the way the data was collected
• Recognizing appropriate graphs for a given data set and its context
• Questioning sample size and methods and their impacts on inferences (i.e. practical significance vs. statistical significant; effect vs. no effect)
• Recognizing when common sources of bias are present in the data collection
• Recognizing appropriate statistics & appropriate representations
• Recognizing and questioning the source of the data including what is quantified and how it was measured

Reading Behind the Data

• Involves
• Using your knowledge of the context of the data to interpret why particular patterns exist in data as well as data generation process (e.g., knowledge of frost in the country through the news)
• Using knowledge of one’s community to interpret why particular patterns exist in the data to question aspects of the data generations process (e.g., knowledge of frost in community)
• Questioning the investigative process undertaken based on personal experiences/identity
• Reimaging the data visualization by considering other ways reality could be quantified and/or collected
• Recognition of the gaps in one’s knowledge [of the context] needed to interpret the statistical message

Reading Behind the Data

• What do you wonder?
• How could we reimagine this data visualization?

Reading Data Visualizations

Resources for Data Visualizations

How-To Document, NYT What’s Going on in this Graph (WGOITG), & Slow Reveal Graph

Creating a Data Visualization Activity for Students

• Link to How-To Document (pdf)

NYT What’s Going on in this Graph

• Joint venture between the New York Times and the American Statistical Association
• Designed to help support classroom teachers in having conversations about data visualizations in the media about current issues
• Each posting includes a notice/wonder prompt to get students making sense of the graph and thinking about and beyond the data
• Students can also comment on NYT webpage and share that with their instructor and can also join a weekly chat with an ASA statistician about the graphic
• https://www.nytimes.com/column/whats-going-on-in-this-graph

Slow Reveal Graph

• Exposes students gradually to the different components of data visualization
• Students are asked to consider what the visualization is showing them before being given more features of the graph
• Each slide deck begins with a naked visualization with a notice/wonder prompt
• Each subsequent slide adds different components of the visualization along with increasingly complex questions to invite discourse amongst students.
• https://slowrevealgraphs.com/

• What do you notice?
• What do you wonder?

• What new information did we just learn?
• How does that change your thinking?

• Now what information do we have?
• What might this race/ethnicity breakdown be about?
• What are your predictions?

• Now what do we know?
• Are you surprised?
• What other information would you like to know?

Source: https://www.pewresearch.org/fact-tank/2023/01/09/u-s-congress-continues-to-grow-in-racial-ethnic-diversity/

Design Your Own Data Visualization Activities

Extending

• Creating slow reveal data visualizations/exploration in CODAP
• For example, https://bit.ly/SRCODAP

Contact Us

• Anita.sundrani1@northwestern.edu
• Tweiland@central.uh.edu

Questions?