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A critical mathematics perspective on reading data visualizations: reimagining through reformatting, reframing, and
Laurie H Rubel1, Cynthia Nicol2, Anna Chronaki3,4
1Department of Mathematics Education, University of Haifa, Haifa, Israel.
Educational Studies in Mathematics
|December 22, 2021
Summary
Data visualizations during the COVID-19 pandemic offer math learning opportunities. A critical reading approach, focusing on mathematical formatting, framing, and narrating, helps unpack their influence.
Area of Science:
- Mathematics Education
- Data Science
- Critical Discourse Analysis
Background:
- Data visualizations became crucial during the COVID-19 pandemic for public health communication and policy.
- These visualizations leverage mathematical concepts, presenting opportunities for mathematics education.
- However, data visualizations are social texts with inherent biases, influencing perceptions and decisions.
Purpose of the Study:
- To present a critical reading approach for data visualizations in mathematics education.
- To equip educators and students with tools to analyze the mathematical and narrative aspects of visualizations.
- To foster a deeper understanding of how data visualizations construct and convey information.
Main Methods:
- Developed a critical reading framework based on three concepts: mathematical formatting, framing, and narrating.
- Applied this framework to analyze COVID-19 data visualizations from the New York Times (2020).
- Proposed guiding questions for a critical analysis of data visualizations.
Main Results:
- Data visualizations are not neutral; they are constructed through specific mathematical choices (formatting, framing) that tell particular stories.
- Analyzing these elements reveals the underlying assumptions and potential impacts of the visualizations.
- The approach allows for reimagining visualizations through reformatting, reframing, and renarrating.
Conclusions:
- A critical reading of data visualizations is essential in mathematics education to understand their construction and influence.
- The proposed framework (formatting, framing, narrating) provides a structured method for this critical analysis.
- This approach encourages a more discerning engagement with data-driven information and promotes data literacy.
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