Data Analytics for Equity: Supporting STEM Faculty to Address Implicit Bias in the Classroom
Effective Years: 2020-2025
This project will study how data visualizations may help university faculty recognize their implicit biases and promote equitable participation in their classrooms. Participation is a key part of learning, but implicit biases can result in different opportunities for different groups of students to participate in STEM classrooms. Such differences can cause inequities in learning outcomes that may lead to underrepresentation of women, students of color, and other groups in the STEM workforce. By studying how to provide STEM faculty with meaningful data about the differential impact of their teaching on student participation, this project has the potential to improve teaching and learning in STEM at scale. This project aims to generate professional development methods and technologies that support equitable teaching across STEM fields. During the project, hundreds of faculty members will receive this professional development, which in turn will impact tens of thousands of students. The project will also develop supporting materials to enable university professional developers and teacher educators to effectively provide similar professional development to the educators they serve. Expected outcomes of this work include new knowledge about equitable teaching in STEM, effective methods for helping instructors teach more equitably, and widespread dissemination of these methods.
This project will directly support over 100 faculty to address implicit bias, by utilizing (and further enhancing) the classroom observation tool, Electronic Quality of Instruction Protocol (EQUIP). EQUIP is a free, web-based classroom observation tool that provides visual representations of classroom participation by different social marker groups. The research plan includes a longitudinal study of a subset of faculty members as they engage in sustained professional development through three interventions: (1) systematic design of data visualizations; (2) a summer institute; and (3) faculty learning communities. Through a mixed-methods research design, this project will address fundamental research topics around how STEM faculty interpret data visualizations, how professional development may leverage visualizations to reduce bias and improve teaching, and the nature of the resulting impacts on student outcomes. The methods will utilize a combination of within- and between-subject comparisons, triangulating data from: classroom observations, data analytics, student outcomes, surveys, and interviews. The major theoretical contributions of this project are to develop knowledge about how data visualizations may improve equitable teaching practices, and to longitudinally study the process through which faculty members go as they learn to recognize and address implicit bias in their teaching. This project can contribute significantly to broadening participation in STEM, because it addresses implicit bias, which is a pervasive problem that limits the participation of women, students of color, people with differing abilities, and other underrepresented groups in STEM. Helping the faculty who participate in the study address their biases is expected to directly produce more equitable learning environments in undergraduate STEM. Moreover, this project will contribute broadly to educator professional development efforts, by documenting and disseminating a suite of tools and best practices for addressing bias and improving equity. The Faculty Early Career Development (CAREER) Program is a National Science Foundation (NSF)-wide activity that supports early-career faculty who have the potential to serve as academic role models in research and education. This CAREER project is supported by NSF's Education & Human Resources Directorate Core Research Program and its Improving Undergraduate STEM Education Program.
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.