2024 ASEE Annual Conference & Exposition

Board 115: Examining Engineering Students’ Gender and Racial Effects in College Course Team Peer Assessment: A Quantitative Intersectional Approach

Presented at Equity, Culture & Social Justice in Education Division (EQUITY) Poster Session

Poster-WIP: Examining Engineering Students’ Gender and Racial Effects in College Course Team Peer Assessment: A Quantitative Intersectional Approach
Keyworks: Peer Assessment; Gender; Race; Intersectionality; Team Performance
Abstract
Peer assessment is commonly employed in college courses embracing team-based learning, with a growing focus on the design’s impact on student learning outcomes. Existing research highlights the influence of factors like gender and race, yet a literature gap persists in understanding how students’ gender and race impact their interactions within small groups and further shape peer assessment in the context of college course teamwork. In this work-in-progress, we employ a quantitative intersectional approach to examine gender and racial effects on peer assessment among over 1,700 engineering college students at a large research-oriented university located in the Midwest. Our analysis indicates a shift in the dominant role of male students, with females playing a more prominent role, particularly among White and Asian students. Gender-based disparities in peer assessment are associated with how White raters evaluate Asian male teammates, highlighting potential biases and the marginalization of Asian males. Furthermore, our findings highlight the underprivileged status of Minoritized groups in engineering education, regardless of their gender. This study stresses the importance of considering gender and race in peer assessment design for evaluating team-based learning outcomes. Moreover, we advocate for the inclusion of group diversity effects in terms of gender and race in future research examining team-based learning and related factors such as designed interventions.ors such as designed interventions.

Authors

Miss Xiaping Li http://orcid.org/0000-0002-2620-1690 University of Michigan [biography]

Xiaping Li is a Ph.D. candidate in Engineering Education Research at the University of Michigan. Her research interests include faculty development and change, neurodiverse college student learning experiences and outcomes, international students in engineering, and cognitive sciences. She holds a B.S. in Hydrology and Water Resources Engineering and an M.S. in Geological Sciences.
Dr. Robin Fowler http://orcid.org/0000-0001-6161-0986 University of Michigan [biography]

Robin Fowler is a Technical Communication lecturer and a Engineering Education researcher at the University of Michigan. Her teaching is primarily in team-based engineering courses, and her research focuses on equity in communication and collaboration as well as in group design decision making (judgment) under uncertainty. She is especially interested in how power relationships and rhetorical strategies affect group judgment in engineering design; one goal of this work is to to understand factors that inhibit full participation of students who identify with historically marginalized groups and investigate evidence-based strategies for mitigating these inequities. In addition, she is interested in technology and how specific affordances can change the ways we collaborate, learn, read, and write. Teaching engineering communication allows her to apply this work as she coaches students through collaboration, design thinking, and design communication. She is part of a team of faculty innovators who originated Tandem (tandem.ai.umich.edu), a tool designed to help facilitate equitable and inclusive teamwork environments.
Dr. Mark Mills http://orcid.org/0000-0002-1145-9592 University of Michigan [biography]

Mark Mills (he/him) is a Data Scientist on the Research & Analytics team at University of Michigan’s Center for Academic Innovation. He directs and supports analytics across CAI’s portfolio of educational technologies. His experience is in prediction and classification of longitudinal and hierarchically cross-classified data structures such as students in courses measured over time.

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