Teamwork is critical to engineering professional work. While some aspects of teaming with engineering students are well understood and implemented into instructional tools, tools for handling student teams dealing with implicit and explicit racism, sexism, and homophobia are infrequent. Instructors of large undergraduate courses need tools to help make team-level marginalization visible at the classroom level to interrupt discriminatory or marginalizing behavior amongst teammates, and to model allyship so teammates learn how to interrupt others' marginalizing behavior when instructors are not around. This paper describes the broader project, and describes some early results, focused on an algorithm that can help identify teams engaging in marginalizing behaviors against minoritized students, whether minoritized by race, gender, nationality, LGBTQ identity, or other categorization schemes. We describe how the algorithm is proving useful to identify student teams to focus on for analysis to answer some of our research questions focused on how engineering undergraduate teams marginalize minoritized members, and illustrate one such analysis. We describe our continuing work on the broader project.
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This content will become publicly available on June 28, 2025
On Faculty Responsibility for Increasing Students’ Sense of Support in the Classroom: Lessons from I-MATTER about Black and Brown Students
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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- Award ID(s):
- 1936778
- PAR ID:
- 10488302
- Publisher / Repository:
- American Society for Engineering Education Papers on Engineering Education Repository
- Date Published:
- Journal Name:
- 2023 ASEE Annual Conference and Exposition
- Format(s):
- Medium: X
- Location:
- Baltimore, MD
- Sponsoring Org:
- National Science Foundation
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