Engaging Students through Data Visualization Activities

Travis Weiland

tweilan1@charlotte.edu

University of North Carolina Charlotte

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

Reading Data Visualization Activity

Launch

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

This map of Forsyth County overlays high-poverty census block groups, in gray, with the location of grocery stories and general stores, such as Target and Walmart. The stars represent “other food stores,” generally convenience marts or dollar stores that primarily sell highly processed, unhealthy food. Some census block groups have no groceries at all. (Map: Tangela Towns, and Richard G. Moye, Winston-Salem State University)

Going Beyond Notice and Wonder

There are different ways you can read a data visualization

• Reading the data
• Reading between the data
• Reading beyond the data
• Reading behind the data

Reading the Data

• What data are displayed in this visualization?
• What is measured in this graph?
• How does this data visualization make you feel?

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 Between the Data

• Why do you think the author chose to highlight this relationship?

• What patterns or trends do you see in the data?

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• What relationship(s) is the author highlighting?

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• How do the relationships displayed here compare to your own experiences?

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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 Beyond the Data

• What story is the author telling with this data visualization?

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• What is a claim you could make from this data visualization?

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• What evidence are you using to make this claim?

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 Behind the Data

• How do you think the data were collected?

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• What other information would help you understand this issue?

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• What other information would help you understand the ways this data impacts you and your community?

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

Going Deeper

• Many data visualization today come from publicly available data or even better data dashboards.
• Using such visualizations can be a great starting point to then digging deeper
• For example

Reflection

Turn and talk with a neighbor

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• How did you read the graph differently based on the questions that were asked?
• How could you use this in your teaching?

How can we do this for our classes?

Step 1: Consider your Learning Objective

• What are your learning objectives for the day?
• How will this activity help you support your students in achieving those objectives?
• Is learning to read data visualizations the main objective?
• Are you using a DV activity to launch an investigation where your goals for the task are to get students engaged in the topic you will be investigating?
• Is the DV activity meant to be a review of concepts students have learned previously or serve as an exit ticket to assess student learning?

Step 2: Select a Data Visualization

• Disclaimer: This step can be deceptively time consuming and challenging

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• Choose a topic that is relevant for your students.
• Choose a data visualization that is within the reach of your students in terms of complexity of the technical language used.
• Choose a data visualization that aligns with your learning objectives.

Levels of Relevance

Where to Find Data Visualizations

• Great starting point is google
• Media, in particular, for really engaging ones try New York Times or Washington Post.
• Local news
• Community organizations
• Governmental organizations

Interesting Data Dashboards

• USDA Food Access Research Atlas
• EPA Environmental Justice Screening and Mapping Tool
• Census Data
• Civil Rights Data Collection Tool
• NC School Report Card Data

Step 3: Select the data visualization practices you want to focus on.

• What types of reading do you want your students to focus on and how does that connect to your standards and learning objectives?
• We have a checklist on our how-to document that is helpful to think this through

Step 4: Choose purposeful questions to guide the reading data visualization discussion.

• What types of reading practices are you aiming for?
• What is in the data visualization?
• What are your learning objectives?
• What questions will meet your students where they are?

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• We have a list on our how-to document in the shared folder that is helpful to get started

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Step 5: Choose how you will teach the data visualization activity

• Quick Launch/warm up
• Launch into full blown data investigation
• Exit ticket
• Stations

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• Let’s take a look at some possible approaches

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/

Data Visualization as a Launch into Data Investigations

• Thinking about data visualization as a starting point to a data investigation
• Lets explore a tool where you can look at lots of different data visualizations and then dive into a data investigation.
• Click this link CODAP Workspace

Data Investigation Process

• Further reading ThinkingDataInvestigationProcess.pdf (instepwithdata.org)
• Further resources: InStep with Data InSTEP | NC State University (instepwithdata.org)

Other Contexts

• Want to consider different data try weather/climate data
• For example, consider this CODAP Workspace
• Or find your own dataset and drag and drop it into CODAP and play

Contact Me

• Got questions
• Need help with CODAP
• Looking for resources
• Send me an email at tweilan1@charlotte.edu

https://bit.ly/NCDataViz