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Collaborative Research: Modules for Statistics Graduate Teaching Assistants Learning to Teach Equitably with Authentic Data (GTAs-LEAD)

Jenny Green PhotoPrincipal Investigators: Jennifer L. Green (MSU); Sunghwan Byun & Justin Post (North Carolina State University)

Graduate Students: Maria Cruciani

Funding: NSF DUE 2315435

Dates: 07/15/2023-06/30/2026

Abstract:  This project aims to serve the national interest by designing and assessing resources for statistics graduate teaching assistants (GTAs). An overall goal is for GTAs to teach equitably with authentic data. Reforming introductory statistics is becoming increasingly important since statistical thinking plays a key role in harnessing the data revolution in a wide range of workplaces and in a variety of disciplines. Despite a call for introductory statistics instruction that: (1) integrates real data with a context and a purpose, (2) teaches statistics as an investigative process, and (3) fosters active learning, many statistics teaching practices are inconsistent with these recommendations. Instead, statistics courses often focus on procedural skills, which may not lead to meaningful understanding of the power of statistical modeling and concepts in authentic contexts. This project will address the urgent need to improve introductory statistics instruction by designing and implementing a set of modules for discipline-specific professional development for statistics GTAs. The significant outcomes of this project could include the development of GTAs who teach equitably with authentic data, as well as advancement of the current understanding of teacher learning of GTAs in professional community settings.

Guided by a design and development research approach, the project will: (1) design a set of four research-informed modules for statistics GTAs learning to teach equitably with authentic data (LEAD Modules), (2) implement LEAD Modules with two GTA communities teaching introductory statistics courses at North Carolina State University and Michigan State University, and (3) further refine LEAD Modules based on design-based research that examines GTA development and their communities. LEAD Modules focus on: (a) teaching statistical thinking, (b) facilitating the model "launch, explore, and discuss," (c) enacting teacher discourse moves, and (d) promoting participatory equity. By drawing on the interdisciplinary expertise of the principal investigators, the project infuses knowledge bases and resources from statistics education and teacher education to support statistics GTAs' learning to teach equitably with authentic data. The NSF IUSE: EDU Program supports research and development projects to improve the effectiveness of STEM education for all students. Through its Engaged Student Learning track, the program supports the creation, exploration, and implementation of promising practices and tools.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.