Research to Practice: Designing Learning Experiences for Teachers around Reading the Word and the World with Data Visualizations

Travis Weiland

2/26/25 AIDEA

Acknowledgement

• This material is based upon work supported by the National Science Foundation under DRK-12 Grant #2143816
• Any opinions, findings, and conclusions or recommendation expressed in this material are those of the author and do not necessarily reflect the view of the National Science Foundation.

Acknowledgement

• A huge thanks goes out to my research assistance over the years
• Dr. Anita Sundrani
• Laura Shelton
• Mandana Delavari
• This work could not be done without the help of many teachers who I have had the privilege of working with over the years and who have given their time.

How do I Situate

• I believe the goal of education is for empowering people to be engaged critical citizens
• I work at the intersections of:
• Mathematics education
• Critical literacy and pedagogy
• Statistics and statistics education
• Data science and data science education

Reading the World with Data

Critical Literacy

• Reading and writing the word and the world (Freire & Macedo, 1987; Gutstein, 2006)
• Reading 
• Making sense of symbol systems
• Identifying and interrogating social structures in the world
• Writing 
• Creating and communicating one’s own meaning through symbol systems 
• Actively influencing and shaping structures in society

Statistical/Data Literacy

• Reading (Gal, 2002)
• Making sense of and critiquing statistical information and data-based arguments
• Evaluating the source, collection and reporting of statistical information
• Writing (Franklin et al., 2007; Lee et al., 2022; Wild & Pfannkuch, 1999) 
• Frame problem
• Collect or consider data
• Process data
• Explore and visualize
• Consider models
• Communicate and propose action

Reading the World with Data

• 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.
• R2. Identifying and interrogating social structures which shape and are reinforced by data-based arguments.
• 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.
• 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.
• R5. Interrogating the epistemological and historical underpinnings of statistical practice and how it has shaped data-based discourses and beyond.

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Guiding Question

How do we create spaces and experiences for students to read the world with data?

Source: DALL-E

Data Visualization—Data Viz

• We use an expansive view of what counts as a data visualization
• Working Definition: A representation of data, in a visual manner, that summaries/syntheses data
• This then includes:
• Infographics
• Maps
• Tables
• Etc.

School Education is a Complex System

Problem

How do we get modern data visualization into the mathematics classroom?

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Source: DALL-E

But What About the Teachers?

• Teachers often only take one statistics course in their undergraduate preparation
• That course is typically only an introduction to statistics courses
• Guess what graphs they typically include in those courses
• Therefore, there is a need for teacher education

Research Questions

• 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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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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• What do you notice?
• What do you wonder?
• What impact does this have on you and your community?
• What’s going on in this map? Write a catchy headline that captures the map’s main idea.

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Reading the World with Data

• 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.
• R2. Identifying and interrogating social structures which shape and are reinforced by data-based arguments.
• 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.
• 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.
• R5. Interrogating the epistemological and historical underpinnings of statistical practice and how it has shaped data-based discourses and beyond.

​

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)

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Helpful but Insufficient

• 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 (Freire, 1970; Gutstein, 2006; Gutierrez, 2013). 
• Drawing from more recent literature (Bailey & McCulloch, 2023; da Silva et al., 2021; Rubel et al., 2016; Rubel et al., 2021)

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Reading Data Visualizations

• 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

Initial Framework

Pilot Study: Context

• Context: University is HSI and AAPISI institution. Took place during a master’s level course on teaching statistics and probability
• Sampling: convenience sampling from a course
• Participants: 8 practicing teachers. All women – 4 white, 2 biracial white and Latina, 1 biracial Black and white, 1 Black
• Wanted to use our framework with practicing teachers because they are more likely to be used to teaching and reading data visualizations in a variety of ways.

Data Visualization 1

https://www.nytimes.com/2023/01/12/learning/whats-going-on-in-this-graph-jan-18-2023.html 

Data Visualization 2

https://www.nytimes.com/2022/10/27/learning/whats-going-on-in-this-graph-nov-9-2022.html 

Data Visualization 3

https://www.nytimes.com/2022/05/05/learning/whats-going-on-in-this-graph-may-11-2022.html 

Study 1: Data and Analysis

• Data: Participants’ responses to 3 discussion board assignments using data visualizations from the NYT “What’s Going on in this Graph?” 
• Responses were coded based on the framework we have shared 
• Both authors coded all the data independently and reconciled for 100% agreement

Study 1: Takeaways

• The type of questions asked lead to different types of reading – useful for curriculum development and teacher educators.
• We needed to think more carefully about the questions we ask.
• We needed to think more carefully about the data visualizations we selected.

Practice: Assessing Questions

• Listed practices by reading type
• Drew questions from recent literature
• Drew upon a common practitioner approach in U.S. in creating assessing questions

Professional Learning Community

• Community of Practice -- Situated Cognition (Lave & Wenger, 1999)
• We went back to Freire’s (1974) literacy work
• Cultural circles
• Generative themes

• Representative/Unrepresentative
• Signal/Noise
• Certainty/Chance

Study Summer 1: Context

• 2-week in-person PD summer of 2023 with 8 secondary mathematics teachers
• focused on critical statistical literacy development 
• Engaged in a series of DV activities embedded in the PD 
• “What’s Going on in this Graph” (New York Times, n.d.) 
• Slow Reveal (Laib, n.d.) instructional routine where aspects of the DV are slowly reveal one slide at a time. 
• Data sources: teachers’ work samples, transcript from a debrief of the slow reveal DV activity, and written reflections from the end of the sixth day.

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Data Visualization Activity 1

Analysis Leads to Revisions

• In analyzing the data from the first summer we found our framework to be insufficient in places
• In particular, there seemed to be more dimensionality to reading the world.
• We went into the literature to make connections

Data Visualization Thread

• There were nine more data visualization activities thread throughout the week
• Many were used as launches into data investigations
• At the end of the second week, we provided guidance as to how to create their own activities

Findings

• 7 of 8 teachers included ≥1 question associated with each reading type. 
• All teachers started with, “what do you notice?” and “what do you wonder?”
• All teachers included questions outside of what we provided

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Findings

• All teachers created a slow reveal DV activity and reported them as a useful activity for their teaching
• “I like how it built curiosity […] I felt like that that curiosity kept building.”
• “if a student got that much information at once like they may gloss over it because it's too much to digest at one time. So the fact that it like forced them to analyze like each piece.”

Findings

• teachers all drew from contexts that were national or global
• All the teachers pick graphs that were similar to the types of graphs they typically teach in a mathematics class

Study School Year 1

• Monthly virtual PLC meetings during the 23-24 school year
• We focused heavily on maintaining and building the community
• We also focused heavily on translating data visualizations into practice
• Based on polling our teachers some used data visualization activities but it was spotty

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Study Summer 2

• Delved deep into a locally relevant theme food access identified by a teacher from discussion with her students
• Invited a local activist/community organizer/expert to discuss the local context of the issue
• Research team created a spatial data visualization in CODAP to read and then explore

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Challenges

• Collecting data, our data is sporadic for different participants
• Designing, analyzing, and redesigning all simultaneously
• Making research-based design frameworks explicit to teachers in useable ways
• Supporting teachers in translating professional learning to classroom practice
• Teachers in the U.S. are so overwhelmed right now anything new or different is just another thing on top of everything else.

Thank You

Please reach out anytime for questions or resources

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tweilan1@charlotte.edu

References

Bailey, N. G., & McCulloch, A. W. (2023). Describing critical statistical literacy habits of mind. Journal of Mathematical Behavior, 70. https://doi.org/10.1016/j.jmathb.2023.101063

da Silva, A. S., Barbosa, M. T. S., de Souza Velasque, L., da Silveira Barroso Alves, D., & Magalhães, M. N. (2021). The COVID-19 epidemic in Brazil: How statistics education may contribute to unravel the reality behind the charts. Educational Studies in Mathematics, 108(1–2), 269–289. https://doi.org/10.1007/s10649-021-10112-6

Freire, P. (1970). Pedagogy of the oppressed. Continuum.

Freire, P., & Macedo, D. (1987). Literacy: Reading the word and the world. Taylor and Francis.

Friel, S. N., Curcio, F. R., & Bright, G. W. (2001). Making sense of graphs: Critical factors influencing comprehension and instructional implications. Journal for Research in Mathematics Education, 32(2), 124–158. https://doi.org/10.2307/749671

Lee, V. R., Wilkerson, M. H., & Lanouette, K. (2021). A Call for a Humanistic Stance Toward K–12 Data Science Education. Educational Researcher, 50(9), 664–672. https://doi.org/10.3102/0013189X211048810

Lim, V. Y., Peralta, L. M. M., Rubel, L. H., Jiang, S., Kahn, J. B., & Herbel-Eisenmann, B. (2023). Keeping pace with innovations in data visualizations: A commentary for mathematics education in times of crisis. ZDM – Mathematics Education, 55(1), 109–118. https://doi.org/10.1007/s11858-022-01449-0

Rubel, L. H., Nicol, C., & Chronaki, A. (2021). A critical mathematics perspective on reading datavisualizations: Reimagining through reformatting,reframing, and renarrating. Educational Studies in Mathematics, 108, 249–268. https://doi.org/10.1007/s10649-021-10087-4

Shaughnessy, M. (2007). Research on statistics learning and reasoning. In F. K. Lester (Ed.), Second handbook of research on mathematics teaching and learning (pp. 957–1009). Information Age Publishing.

Weiland, T. (2017). Problematizing statistical literacy: An intersection of critical and statistical literacies. Educational Studies in Mathematics, 96(1), 33–47. https://doi.org/10.1007/s10649-017-9764-5