Statistics

Dancing with Data: Embodying the Numerical and Humanistic Sides of Data

Data literacy is important for supporting individuals to incorporate information from research studies into their own perspectives and decision-making processes. However, it can be challenging for students to read, understand, and relate to data. Students have to be able to traverse the representational forms that data takes on (i.e., numerical, graphical, etc.) and connect it to their understanding of a topic.

Author/Presenter

Kayla Desportes

Ralph Vacca

Marian Tes

Peter J. Woods

Camillia Matuk

Anna Amato

Megan Silander

Year
2022
Short Description

We explore the implementation of a co-designed data-dance unit in which middle school students created their own embodied metaphors to represent and communicate about graphs through dance. In analyzing dance artifacts and post-study interviews with the learners and teachers, we demonstrate how the creation of embodied metaphors in dance led to new ways of exploring the data as learners reflected on different perspectives on topics across numerical values, contexts, and implications.

Dancing with Data: Embodying the Numerical and Humanistic Sides of Data

Data literacy is important for supporting individuals to incorporate information from research studies into their own perspectives and decision-making processes. However, it can be challenging for students to read, understand, and relate to data. Students have to be able to traverse the representational forms that data takes on (i.e., numerical, graphical, etc.) and connect it to their understanding of a topic.

Author/Presenter

Kayla Desportes

Ralph Vacca

Marian Tes

Peter J. Woods

Camillia Matuk

Anna Amato

Megan Silander

Year
2022
Short Description

We explore the implementation of a co-designed data-dance unit in which middle school students created their own embodied metaphors to represent and communicate about graphs through dance. In analyzing dance artifacts and post-study interviews with the learners and teachers, we demonstrate how the creation of embodied metaphors in dance led to new ways of exploring the data as learners reflected on different perspectives on topics across numerical values, contexts, and implications.

Dancing with Data: Embodying the Numerical and Humanistic Sides of Data

Data literacy is important for supporting individuals to incorporate information from research studies into their own perspectives and decision-making processes. However, it can be challenging for students to read, understand, and relate to data. Students have to be able to traverse the representational forms that data takes on (i.e., numerical, graphical, etc.) and connect it to their understanding of a topic.

Author/Presenter

Kayla Desportes

Ralph Vacca

Marian Tes

Peter J. Woods

Camillia Matuk

Anna Amato

Megan Silander

Year
2022
Short Description

We explore the implementation of a co-designed data-dance unit in which middle school students created their own embodied metaphors to represent and communicate about graphs through dance. In analyzing dance artifacts and post-study interviews with the learners and teachers, we demonstrate how the creation of embodied metaphors in dance led to new ways of exploring the data as learners reflected on different perspectives on topics across numerical values, contexts, and implications.

"I Happen to Be One of 47.8%": Social-Emotional and Data Reasoning in Middle School Students' Comics about Friendship

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships. The 7th grade unit involved students analyzing data graphs about adolescent friendships and crafting comic narratives to convey perspectives on that data.

Author/Presenter

Ralph Vacca

Kayla Desportes

Marian Tes

Megan Silander

Camillia Matuk

Anna Amato

Peter J. Woods

Year
2022
Short Description

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships.

"I Happen to Be One of 47.8%": Social-Emotional and Data Reasoning in Middle School Students' Comics about Friendship

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships. The 7th grade unit involved students analyzing data graphs about adolescent friendships and crafting comic narratives to convey perspectives on that data.

Author/Presenter

Ralph Vacca

Kayla Desportes

Marian Tes

Megan Silander

Camillia Matuk

Anna Amato

Peter J. Woods

Year
2022
Short Description

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships.

"I Happen to Be One of 47.8%": Social-Emotional and Data Reasoning in Middle School Students' Comics about Friendship

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships. The 7th grade unit involved students analyzing data graphs about adolescent friendships and crafting comic narratives to convey perspectives on that data.

Author/Presenter

Ralph Vacca

Kayla Desportes

Marian Tes

Megan Silander

Camillia Matuk

Anna Amato

Peter J. Woods

Year
2022
Short Description

Effective data literacy instruction requires that learners move beyond understanding statistics to being able to humanize data through a contextual understanding of argumentation and reasoning in the real-world. In this paper, we explore the implementation of a co-designed data comic unit about adolescent friendships.

Digging into Data: Illustrating a Data Investigation Process

Lee, H.S., Mojica, G. M., & Thrasher, E. (2022). Digging into data: Illustrating an investigative process. Statistics Teacher.

Author/Presenter

Hollylynne S. Lee

Gemma F. Mojica

Emily Thrasher

Year
2022
Short Description

In this article, authors described the six-phase data investigation process for analyzing large-scale quantitative and categorical data.

Investigating Data Like a Data Scientist: Key Practices and Processes

With a call for schools to infuse data across the curriculum, many are creating curricula and examining students’ thinking in data-intensive problems. As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists.

Author/Presenter

Hollylynne Lee

Gemma Mojica

Emily Thrasher

Peter Baumgartner

Year
2022
Short Description

As the discipline of statistics education broadens to data science education, there is a need to examine how practices in data science can inform work in K-12. We synthesize literature about statistics investigation processes, data science as a field and practices of data scientists. Further, we provide results from an ethnographic and interview study of the work of data scientists.

Data Investigations to Further Social Justice Inside and Outside of STEM

This article focuses on discussion and preliminary findings from classroom testing of the prototype learning module: Investigating Income Inequality in the U.S. In this module, students examine patterns of income inequality using person-level microdata from the American Community Survey (ACS) and the U.S. decennial census.

Author/Presenter

Josephine Louie

Jennifer Stiles

Emily Fagan

Soma Roy

Beth Chance

Year
2021
Short Description

This article focuses on discussion and preliminary findings from classroom testing of the prototype learning module: Investigating Income Inequality in the U.S.

Investigating Immigration to the U.S.: Module Overview and Sample Lessons

The Investigating Immigration to the U.S. module focuses on describing, comparing, and making sense of categorical variables. Students investigate questions such as: Are there more immigrants in the U.S. today than in previous years? Where have most immigrants been coming from? Are immigrants as likely as the U.S. born to be participating in the labor force?

Author/Presenter

SDLC Project Team

Year
2020
Short Description

This sample document contains 1) an overview of the module lessons and learning objectives; 2) the teacher guide for Lesson 4, titled Are there more immigrants in the U.S. today than in previous years?; 3) the teacher guide for Lesson 5, titled Where have most immigrants been coming from?;and 3) the team data investigation.