Statistics

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.

Building Statistical Thinking with Social Justice Investigations and Social Science Data

This poster provides an overview of the Strengthening Data Literacy across the Curriculum (SDLC) project, which is developing and studying curriculum modules for non-AP high school statistics classes to promote interest and skills in statistical thinking and data science among diverse high school populations. This early-stage design and development project aims to engage students with data investigations that focus on issues of social justice, using large-scale socioeconomic data from the U.S. Census Bureau and student-friendly online data visualization tools.

Author/Presenter

Josephine Louie

Beth Chance

Soma Roy

Emily Fagan

Jennifer Stiles

William Finzer

Year
2020
Short Description

This poster provides an overview of the Strengthening Data Literacy across the Curriculum (SDLC) project, which is developing and studying curriculum modules for non-AP high school statistics classes to promote interest and skills in statistical thinking and data science among diverse high school populations. This early-stage design and development project aims to engage students with data investigations that focus on issues of social justice, using large-scale socioeconomic data from the U.S. Census Bureau and student-friendly online data visualization tools. Primary social justice topics are income inequality and immigration in the U.S. This poster was created for the SREE Spring 2020 Conference.

American Statistical Association 2020 Joint Statistical Meetings; Philadelphia, PA; Aug 1-6, 2020 - VIRTUAL

Event Date
-
Sponsoring Organization

Due to the COVID-19 pandemic, this conference will be held virtually.

To learn more, visit https://ww2.amstat.org/meetings/jsm/2020/.

DRK-12 Presenters:

  • Beth Chance and Soma Roy, Cal Poly State University; Josephine Louie, Education Development Center (EDC); Willian Finzer, Concord Consortium; Emily Fagan and Jennifer Stiles, Education Development Center (EDC)
Discipline/Topic
Event Type

Leveraging Open Source Tools across NSF-funded Projects: Partnerships, Integration Models, and Developer Communities

STEM Categorization
Day
Fri

Discuss the potential utility of CODAP and other open source tools in your work, effective cross-project partnerships, and supporting developer communities around open source materials.

Date/Time
-
Session Materials

Goal: Participants will explore the spectrum of “working together” from collaboration to community. Alongside participant examples, CODAP will be used as a model to explore the range of possibilities.

Objectives: That participants

Session Types

Webinar on the Common Guidelines for Education Research and Development

Author/Presenter

Edith Gummer

Year
2014
Short Description

This webinar, led by Edith Gummer (formerly of NSF), discusses the guidelines outlined in the report co-authored by the Institute of Education Sciences, U.S. Department of Education and the National Science Foundation.

Learning as a Community: Maximizing the Impact of Research Syntheses in Science Education

Day
Tues

This interactive session is designed to promote critical thinking about current research practices and integrate a variety of perspectives on research syntheses and how they can help advance education research.

Date/Time
-
2014 Session Types
Collaborative Panel Session
Session Materials

Examples of research practices that limit the validity of research syntheses are not difficult to find. For example, Education Development Center, Inc. (EDC), and Abt Associates reported in their Compendium of STEM Instruments that psychometric reporting practices in the STEM community tend to be insufficient, and this limited what they could learn from their synthesis.