Empowering Educators: Supporting Equitable Teaching of Statistics & Data Science Across Grades K-16

Abstract Number:

1579 

Submission Type:

Topic-Contributed Paper Session 

Participants:

Jennifer Green (1), Christine Franklin (2), Maria Cruciani (1), Stephanie Casey (3), Soma Roy (4), Sunghwan Byun (5), Rick Hudson (6)

Institutions:

(1) Michigan State University, N/A, (2) University of Georgia, N/A, (3) Eastern Michigan University, N/A, (4) California Polytechnic State University, N/A, (5) North Carolina State University, N/A, (6) University of Southern Indiana, N/A

Chair:

Maria Cruciani  
Michigan State University

Discussant:

Christine Franklin  
University of Georgia

Session Organizer:

Jennifer Green  
Michigan State University

Speaker(s):

Stephanie Casey  
Eastern Michigan University
Soma Roy  
California Polytechnic State University
Sunghwan Byun  
North Carolina State University
Rick Hudson  
University of Southern Indiana

Session Description:

Statistics and data science literacy are important for students across all levels of education, enabling them to think critically about and use data to address authentic problems and issues affecting society (e.g., PreK-12 GAISE II, GAISE College Report). Teachers play a critical role in supporting students' learning and development in these areas, but many receive little to no preparation in the teaching and learning of statistics and data science (Justice et al., 2017; Lovett & Lee, 2017), let alone in how to teach equitably with authentic data. With the growing need to develop students' literacy and agency in using data to question and address societal issues and concerns, it's critical that the statistics and data science education community support teachers of statistics and data science across all levels of education. In this topic-contributed paper session, each speaker will present innovative ways to support teachers' preparation and development, with a specific focus on how to support them in teaching statistics and data science equitably with authentic data concerning societal issues. By discussing multiple levels of education, this session will offer attendees useful insight on available resources for, existing models of, and future work in transforming the teaching and learning of statistics and data science education for grades K-16 and beyond. In this session, the first paper, "Preparing Preservice Mathematics Teachers to Teach Statistics and Data Science: Technology, Equity, and Pedagogical Content Knowledge Considerations", will address the development of preservice K-12 teachers of statistics and data science. The second paper, "Empowering Teachers to Integrate Statistical Thinking and Reasoning in High School Mathematics Classes", will attend to in-service K-12 teacher development. The third paper, "Equipping Statistics GTAs with Teaching Practices for Equitable Instruction", will focus on graduate teaching assistant development, and the fourth paper, "Development of Teacher Educators to Prepare Teachers to Teach Statistics and Data Science", will discuss the development of teacher educators and faculty. The session will then end with the discussant who is a leader in K-16 statistics and data science education.

References:
Justice, N., Zieffler, A., & Garfield, J. (2017). Statistics graduate teaching assistants' beliefs, practices and preparation for teaching introductory statistics. Statistics Education Research Journal, 16(1), 294-319.
Lovett, J. N., & Lee, H. S. (2017). New standards require teaching more statistics: Are preservice secondary mathematics teachers ready? Journal of Teacher Education, 68(3), 299-311.

Sponsors:

Justice Equity Diversity and Inclusion Outreach Group 3
Section on Statistics and Data Science Education 1
Section on Teaching of Statistics in the Health Sciences 2

Theme: Statistics and Data Science: Informing Policy and Countering Misinformation

No

Applied

Yes

Estimated Audience Size

Medium (80-150)

I have read and understand that JSM participants must abide by the Participant Guidelines.

Yes

I understand and have communicated to my proposed speakers that JSM participants must register and pay the appropriate registration fee by June 1, 2024. The registration fee is nonrefundable.

I understand