Statistics

Online Teacher Professional Learning: An Approach to Foster Personalized Pathways

Teachers tend to be lifelong learners, motivated to pursue professional learning that is meaningful to their particular needs. In 2013, Marrongelle et. al., noted “it is incumbent on the field to capitalize on emerging technologies in the design and delivery of effective professional development.” (p. 208). While the past decade has seen an increase in development of opportunities for personalized learning for mathematics teachers online (e.g., Silverman & Hoyos, 2018), more work is needed to provide additional research-based opportunities.

Author/Presenter

Hollylynne S. Lee

Emily P. Thrasher

Matt Grossman

Gemma F. Mojica

Bruce Graham

Adrian Kuhlman

Year
2022
Short Description

The InSTEP professional learning platform aims to support grades 6-12 teachers’ professional learning in teaching statistics and data science through a personalized online learning platform. While statistics and data analysis are included in standards for both mathematics and science, there are also many states across the country envisioning high school course pathways that include a heavier emphasis on statistics and even stand alone courses on data science. In this brief research report, we aim to share how we have designed supports for teachers to personalize their professional learning and results from a collective case study of 37 participants engaged in a field test of the platform in Fall 2022.

Engagement in the InSTEP Professional Learning Platform: Developing Expertise to Teach Data and Statistics

In this study, 82 middle and high school teachers engaged with the InSTEP online professional
learning platform to develop their expertise in teaching data science and statistics. We
investigated teachers’ engagement within the platform, aspects of the platform that were most
and least effective in building teachers’ expertise, and the extent to which teachers’ self-efficacy
changed. Using mixed methods, we collected, analyzed and integrated multiple data sources.

Author/Presenter

Gemma F. Mojica

Emily Thrasher

Adrian Kuhlman

Bruce Graham

Hollylynne S. Lee

Michelle Pace

Year
2023
Short Description

In this study, 82 middle and high school teachers engaged with the InSTEP online professional
learning platform to develop their expertise in teaching data science and statistics. We
investigated teachers’ engagement within the platform, aspects of the platform that were most
and least effective in building teachers’ expertise, and the extent to which teachers’ self-efficacy
changed.

Opportunities for Research within the Data Science Education Community

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

Author/Presenter

Katherine Miller, Chad Dorsey, The Concord Consortium; Kirsten Daehler, Leti Perez, WestEd; Kayla DesPortes, New York University; Nicholas Horton, Amherst College; Seth Jones, Middle Tennessee State University; Josephine Louie, Education Development Center; Josh Rosenberg, University of Tennessee, Knoxville; David Weintrop, University of Maryland

Lead Organization(s)
Year
2023
Short Description

This webinar provided early career data science education researchers with information on the state of the field; tools, curricula, and other resources for researchers; and insight into funding opportunities and proposal development. Participants explore topics, research interests, and problems of practice in more depth in breakout rooms with session leaders.

Preparing for a Data-Rich World: Civic Statistics Across the Curriculum

Civic Statistics by its nature is highly interdisciplinary. From a cross-curricular perspective, teaching and learning Civic Statistics faces specific challenges related to the preparation of teachers and the design of instruction. This chapter presents examples of how Civic Statistics resources and concepts can be used in different courses and subject areas. Because topical issues and current data are central to these resources, we recognise that the original ProCivicStat resources will become outdated in time.

Author/Presenter

Joachim Engel

Josephine Louie

Year
2023
Short Description

Civic Statistics by its nature is highly interdisciplinary. From a cross-curricular perspective, teaching and learning Civic Statistics faces specific challenges related to the preparation of teachers and the design of instruction. This chapter presents examples of how Civic Statistics resources and concepts can be used in different courses and subject areas.

Advancing Social Justice Learning Through Data Literacy

Students need “critical data literacy” skills to help make sense of the multitude of information available to them, especially as it relates to high-stakes issues of social justice. The authors describe two curriculum modules they developed—one on income equality, one on immigration—that help students learn to analyze data in order to shed light on complex social issues and evaluate claims about those issues.

Author/Presenter
Josephine Louie

Emily Fagan

Jennifer Stiles

Soma Roy

Beth Chance

Year
2023
Short Description

Students need “critical data literacy” skills to help make sense of the multitude of information available to them, especially as it relates to high-stakes issues of social justice. The authors describe two curriculum modules they developed—one on income equality, one on immigration—that help students learn to analyze data in order to shed light on complex social issues and evaluate claims about those issues.

Building Toward Critical Data Literacy with Investigations of Income Inequality

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States. Designed as a multi-week set of applied data investigations, the module supports student analyses of income inequality using U.S. Census Bureau microdata and the online data analysis tool the Common Online Data Analysis Platform (CODAP).

Author/Presenter

Josephine Louie

Jennifer Stiles

Emily Fagan

Beth Chance

Soma Roy

Year
2022
Short Description

To promote understanding of and interest in working with data among diverse student populations, we developed and studied a high school mathematics curriculum module that examines income inequality in the United States.

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.