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WIP: Identity-Based Bias in Undergraduate Peer Assessment

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Conference

2022 First-Year Engineering Experience

Location

East Lansing, Michigan

Publication Date

July 31, 2022

Start Date

July 31, 2022

End Date

August 2, 2022

Conference Session

Technical Session M1

Tagged Topics

Diversity and Works In Progress

Page Count

5

DOI

10.18260/1-2--42257

Permanent URL

https://peer.asee.org/42257

Download Count

220

Paper Authors

biography

Madison Jeffrey University of Michigan

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Madison Jeffrey is a graduate candidate in the University of Michigan's Masters in Higher Education program. With a focus on Management and Organizations, she's interested in ways in which the system of higher education can adapt to become more accessible and equitable to students. She's a research assistant at the University of Michigan's College of Engineering, where she works with a team of researchers responsible for Tandem, a software that monitors team performance to link students and instructors.

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biography

Robin Fowler University of Michigan

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Robin Fowler is a lecturer in the Program in Technical Communication at the University of Michigan. She enjoys serving as a "communication coach" to students throughout the curriculum, and she's especially excited to work with first year and senior students, as well as engineering project teams, as they navigate the more open-ended communication decisions involved in describing the products of open-ended design scenarios.

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biography

Mark Mills UM, Center for Academic Innovation Orcid 16x16 orcid.org/0000-0002-1145-9592

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Mark Mills is a Data Scientist with the Center for Academic Innovation at the University of Michigan. He is responsible for leading analysis across the Center in support of its mission to leverage data for shaping innovation in higher education. Mark received his PhD from the University of Nebraska in Cognitive and Quantitative Psychology, where he studied models for classifying cognitive state from eye movements.

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Abstract

Peer evaluation is a commonly used practice for group work throughout higher education as it allows students to provide and receive insight as well as provides additional information to faculty. When such peer evaluations may be taken into consideration for grades, it is especially important to look closer at the potential bias that may be present. Students may unconsciously perceive members of their group in biased ways because of ingrained gender or racial biases, potentially resulting in skewed feedback. In this project, we look at how student feedback varies for students with historically underrepresented identities. To accomplish this, we will use survey data from a software tool that monitors team members throughout the course of a project to provide a link between students and instructors. Part of this software allows team members to evaluate one another and provide feedback, including items such as reliability, effort, quality of work, and idea generation. Using hierarchical linear regression, we will investigate patterns of ratings based on identity characteristics such as gender and race (of both evaluator and evaluated) to investigate how these factors are related to evaluations.

Jeffrey, M., & Fowler, R., & Mills, M. (2022, July), WIP: Identity-Based Bias in Undergraduate Peer Assessment Paper presented at 2022 First-Year Engineering Experience, East Lansing, Michigan. 10.18260/1-2--42257

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