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Teaching engineering students how to work in teams is necessary, important, and hard to do well. Minoritized students experience forms of marginalization from their teammates routinely, which affects their access to safe learning environments. Team evaluation tools like CATME can help instructors see where teaming problems are, but are often normed in ways that obscure the subtle if pervasive harassment of minoritized teammates. Instructors, particularly of large courses, need better ways to identify teams that are marginalizing minoritized team members. This paper introduces theory on microaggressions, selective incivility theory, and coded language to interpret data collected from a complex study site during the COVID-19 pandemic. The team collected data from classroom observations (moved virtual during COVID), interviews with instructors, interviews with students, interpretations of historical data collected through an online team evaluation tool called CATME, and a diary study where students documented their reflections on their marginalization by teammates. While data collection and analysis did not, of course, go as the research team had planned, it yielded insights into how frequently minoritized teammates experience marginalization, instructors’ sense of their responsibility and skill for addressing such, marginalization, and students’ sense of defeat in hoping for more equitable and supportive learning environments. The paper describes our data collection processes, analysis, and some choice insights drawn from this multi-year study at a large, research-extensive white university.
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Is Carla grumpy? Analysis of peer evaluations to explore microaggressions and other marginalizing behaviors in engineering student teams
Abstract BackgroundTeamwork has become a central element of engineering education. However, the race‐ and gender‐based marginalization prevalent in society is also prevalent in engineering student teams. These problematic dynamics limit learning opportunities, isolate historically marginalized students, and ultimately push students away from engineering, further reinforcing the demographic imbalances in the profession. PurposeWhile there are strategies to improve the experiences of marginalized students within teams, there are few tools for detecting marginalizing behaviors as they occur. The purpose of this work is to examine how peer evaluations collected as a normal part of an engineering course can be used as a window into team dynamics to reveal marginalization as it occurs. MethodWe used a semester of peer evaluation data from a large engineering course in which a team project is the central assignment and peer evaluation occurs four times during the course. We designed an algorithm to identify teams where marginalization may be occurring. We then performed qualitative analyses using a sociolinguistic analysis. ResultsResults show that the algorithm helps identify teams where marginalization occurs. Qualitative analyses of four illustrative cases demonstrated the stealth appearance and evolution of marginalization, providing strong evidence that hidden within language of peer evaluation are indicators of marginalization. Based on the wider dataset, we present a taxonomy (eight categories) of linguistic marginalization appearing in peer comments. ConclusionBoth peer evaluation scores and the language used in peer evaluations can reveal team inequities and may serve as a near‐real‐time mechanism to interrupt marginalization within engineering teams.
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- Award ID(s):
- 1936778
- PAR ID:
- 10524373
- Publisher / Repository:
- Wiley Blackwell (John Wiley & Sons)
- Date Published:
- Journal Name:
- Journal of Engineering Education
- Volume:
- 113
- Issue:
- 3
- ISSN:
- 1069-4730
- Format(s):
- Medium: X Size: p. 603-634
- Size(s):
- p. 603-634
- Sponsoring Org:
- National Science Foundation
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