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Supporting Reading Data Visualizations: Going Beyond Notice and Wonder

Anita Sundrani and Travis Weiland

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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.�

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Graphs We See in Math Class

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Graphs We See Outside of Math Class

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What Happening

Why don’t we see data visualizations in the mathematics curriculum like those that we see in our everyday lives if we are preparing students to be citizens?

Change is hard???

Education is slow to change???

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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

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Clear Rationale

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Not Just about Reading Graphs

It's about making sense of your world
It's about reading behind the data visualization in how it was created
It's about reading into the author and the story they are trying to tell
It's about reading the word and the world

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So what does this look like and how do we make it happen?

My attempts at connecting theory and practice

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Theory

A wonderful space to play where anything is possible

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Reading and Writing the World with Data�

Critical Literacy

Data/Statistical Literacy

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Critical Statistical Literacy (Weiland, 2017; Weiland & Sundrani, 2022)

Reading	Writing
R1. Making sense of language and statistical symbols systems and critiquing statistical information and data-based arguments encountered in diverse contexts to gain an awareness of the systemic structures at play in society.	W1. Using statistical investigations to communicate statistical information and arguments in an effort to destabilize and reshape structures of injustice for a more just society.
R2. Identifying and interrogating social structures which shape and are reinforced by data-based arguments.	W2. Using statistical investigations to alleviate and resolve sociopolitical issues of injustice.
R3. 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.	W3. Negotiating societal dialectical tensions when formulating statistical questions, data collection and analysis methods and highlighting such tensions in the results of a statistical investigation.
R4. Evaluating the source, operationalization, collection and reporting of statistical information and how they are influenced by the author’s social position, and sociopolitical and historical lens.	W4. Communicating one’s social location, subjectivity, and political context to others and how it shapes one’s meaning making of the world when reporting results of a statistical investigation.
R5. Interrogating the epistemological and historical underpinnings of statistical practice and how it has shaped data-based discourses and beyond.	W5. Using sociopolitical oriented epistemologies to create new statistical practices and ways of measuring the world.

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Theory to Practice

How do we support teachers in developing critical statistical literacies for reading data visualizations common in media and society today?
How do we support teachers in providing students with opportunities to experience reading data visualizations critically?

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Practice

The cold harsh reality of the social, historical, and spatial moment we are situated in

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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

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Launch

What do you notice?
What do you wonder?
How does this relate to you and your community?

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Initial Findings

In responding to prompts we saw patterns in what teachers would attend to in the graphs:

What do you notice🡪 reading off labels or points on the graph or sometimes a clear relationship being explicitly presented
What do you wonder🡪 questioning where the data comes from
What impact does this have on you and your community🡪 Connecting what they were inferring from the data to their lived experiences
Write a catchy headline that captures the map’s main idea🡪 Making claims on what the data shows

Most of the things that teachers attended to were very surface level.

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Problems: Theoretical and Practical

Our theory was too broad to support analyzing the data to capture what they were doing.
Our theory is also not one of development, which is necessary to think about how people develop critical data reading practices
We needed more specific practices for reading data visualizations that could also capture development over time
From this we also needed better designed activities to create opportunities for teachers to engage in such practices

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Theory

We went back to the drawing board and read some more

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Back to the Literature

Past scholarship has emphasized an explicit focus on reading graphs to support graph comprehension (Curcio 1981; Friel et al., 2001, Shaughnessy, 2007)

Reading Level	Description
Reading the data (Friel et al., 2001)	Lifting information from the graph to answer explicit questions for which the obvious answer is in the graph
Reading between data (Friel et al., 2001)	Interpretation and integration of information that is presented in a graph – the reader completes at least one step of logical or pragmatic inferring to get from the question to the answer
Reading beyond the data (Friel et al., 2001)	Extending, predicting, or inferring from the representation to answer questions – the reader gives an answer that requires prior knowledge about a question that is related to the graph
Reading behind the data (Shaughnessy, 2007, as cited in Rubel et al., 2016)	Interpretations of why particular patterns exist in the data as well as questioning the sources of the data, the sampling used to generate it, and other factors

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Updating the Framework

The reading graph levels framework described by Friel et al. (2001) and Shaughnessy (2007) was helpful but not sufficient
Designed to be hierarchical, but did not fit what we saw in data or our perspective on learning
Boundary of between and beyond is murky
Was missing a critical literacy lens

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Critical Statistical Literacy Habits of Mind (Bailey & McCulloch, 2023)

Habit of Mind	Description
Questioning Sample Size and Methods	Individual demonstrates healthy skepticism regarding the sample, sample size, sampling technique, sampling bias, or lack of information regarding sampling that may lead to invalid inference on a target population.
Recognizing Appropriate Statistics & Appropriate Representations	Individual questions whether the type of statistics and/or the way it is represented is the most appropriate for the data.
Desiring Additional Information	Individual demonstrates a need for additional information to draw a reasonable conclusion.
Acknowledging Alternate Explanations	Individual acknowledges the potential for alternative interpretations for the meaning of findings or different explanations for what caused them
Recognition of One’s Own Sociopolitical/ Critical Consciousness	Individual recognizes how they are integrating their own social, political, economic, etc. understandings to make sense of injustice within the statistical message.
Employing Active Citizenry	Individual is aware of inequities within the statical message. Individual expresses a desire to disrupt and dismantle inequities

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Critical Mathematics Perspective on Reading Data Visualizations (Rubel et al., 2021)

Critical reading of data visualizations:

Narrating: What story is the author telling with this data visualization?
Framing: Which relationship is the author highlighting and with what visualization?
Formatting: What has the author quantified? How has the author defined the measurements?

Reimagining data visualizations

Renarrating: What stories could be told?
Reframing: What relationships could be highlighted or visualized?
Reformatting: What could be quantified and what data would be necessary?

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Notice, Wonder, Feel, Act, and Reimagine as a Path Toward Social Justice in Data Science Education (Kahn et al., 2022)

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Types of Reading Data Visulaizations

We dropped the idea of levels and instead focus on different types of reading
The overall descriptions are updated but similar to what was in the original framework
The bulk of the work was in the practices

Reading Type	Description
Reading the Data	Locate and extract relevant information from data visualization
Reading Between the Data	Find patterns or relationships in the data visualization
Reading Beyond the Data	Move beyond the data visualization to making predictions or inferences, answering a question
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

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Reading the Word and the World

We begin with reading the world around us and build more technical understandings of reading the word
Reading the world bootstraps reading the word, which in turn bootstraps reading the world
Three categories to this dimension (drawing upon Freire, 1970; Ladson-Billings, 1995; Lee et al., 2021)

Reading the Word: Technical reading of graphs focused on the discourse of the discipline of statistics and data science
Reading the World Personal/Community: Reading graphs making explicit connections between past experiences and communities
Reading the World Sociopolitical: Making explicit connections to broader discourses or questioning the subjectivity of the author and creation of the data visualization

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Practice

We went back to try things out. We are design researchers after all.

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Practitioner Framing

Reading Type	Description	Practices	Assessing Questions
Reading between the data	Find patterns or relationships in the data visualization	Identify and discuss the relationships between data representations (i.e. table, graph, dataset, statistics, etc.) Discussing patterns of relationships identified in the data visualization Recognizing the types of relationships (correlational or causal) that can be claimed based on the data collection methods Using personal experiences to discuss how you are interpreting/connecting to the patterns/relationships you see the data visualization Identifying and questioning how the author has highlighted particular relationships/trends in the data visualization	What is the relationship between the variables in this visualization? What do you think of the difference between the two categories? What other patterns/trends do you see in the data? What new information did we just learn?  How do the relationships displayed here compare to your own experiences? How has the author created the data visualization to highlight relationships/trends? Why do you think the author chose to highlight this relationship?

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Reading the Data

What data are displayed in this visualization?
How does this data visualization make you feel?
What do you think the purpose of this data visualization is?
What relationship(s) is the author highlighting?
What do you think the purpose of this data visualization is?
Why do you think the author ordered the data in this way?
What are the advantages of including both amounts and percentages in this data visualization? Disadvantages?

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Reading Data Visualizations

Why focus on different levels of reading data?

How would you use this framework in your teaching?

What are some challenges you might have?

What questions do you have?

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Creating a Data Visualization Activity for Students

In the shared folder you will find a how to document we have created to support teachers in creating data visualization activities.

https://bit.ly/AMTE24DataViz

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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/

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What do you notice?
What do you wonder?

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

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

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

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

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Future Directions in Design

Data visualization activities make for good warm up or launch activities but then what?
Creating slow reveal data visualizations/exploration in CODAP
For example, https://bit.ly/DATASR

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Questions?�tweiland@uh.edu