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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How Can We Identify Teams at Risk of Marginalizing Minoritized Students, at Scale?
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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- Award ID(s):
- 1936778
- PAR ID:
- 10297862
- Publisher / Repository:
- American Society for Engineering Education Papers on Engineering Education Repository
- Date Published:
- Journal Name:
- ASEE Annual Conference proceedings
- ISSN:
- 1524-4644
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Teaming is increasingly important to teach well in undergraduate engineering education. Teams composed of both majority and minoritized students have an increased risk of majority members harassing minoritized members. Instructors of large classes have a difficult time identifying in which teams such harassment is taking place, and knowing what to do to interrupt it. This paper, part of a bigger project grounded in microaggression theory and selective incivility theory, specifically considers what instructors currently do, and indeed whether it is their job to address teammate harassment. We undertook a rough thematic analysis of interviews with instructors of a large first-year engineering course at a large American research-extensive majority-white university in the Midwest. We found instructors adopted an individual-centric model of teaming, intervened mainly in severe instances, and their interventions tended to be subtle. We offer an early version of an alternative model to structure forthcoming training sessions with instructors, graduate teaching assistants, and peer teachers.more » « less
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Abstract Background Teamwork 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.
Purpose While 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.
Method We 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.
Results Results 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.
Conclusion Both 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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A. Weinberg, D. Moore-Russo (Ed.)Undergraduate mathematics instruction contributes to marginalization among women and racially minoritized individuals’ experiences. This report presents an analysis from a larger study that details variation in minoritized students’ perceptions of potentially marginalizing events in undergraduate mathematics instruction. With past research on undergraduate mathematics experiences largely focused on students’ post-hoc reflections and one or two race-gender intersections, this analysis extends prior work by attending to variation in students’ in-the-moment perceptions of mathematics instruction across various race-gender intersections. Findings highlight how issues of underrepresentation, stereotypes, and instructor care contributed to interpretations of instruction-related events as potentially marginalizing. The report concludes with implications for teaching practices in undergraduate mathematics that academically support and socially affirm students from historically marginalized backgrounds.more » « less
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Jeff Nichols (Ed.)Instructors using algorithmic team formation tools must decide which criteria (e.g., skills, demographics, etc.) to use to group students into teams based on their teamwork goals, and have many possible sources from which to draw these configurations (e.g., the literature, other faculty, their students, etc.). However, tools offer considerable flexibility and selecting ineffective configurations can lead to teams that do not collaborate successfully. Due to such tools’ relative novelty, there is currently little knowledge of how instructors choose which of these sources to utilize, how they relate different criteria to their goals for the planned teamwork, or how they determine if their configuration or the generated teams are successful. To close this gap, we conducted a survey (N=77) and interview (N=21) study of instructors using CATME Team-Maker and other criteria-based processes to investigate instructors’ goals and decisions when using team formation tools. The results showed that instructors prioritized students learning to work with diverse teammates and performed “sanity checks” on their formation approach’s output to ensure that the generated teams would support this goal, especially focusing on criteria like gender and race. However, they sometimes struggled to relate their educational goals to specific settings in the tool. In general, they also did not solicit any input from students when configuring the tool, despite acknowledging that this information might be useful. By opening the “black box” of the algorithm to students, more learner-centered approaches to forming teams could therefore be a promising way to provide more support to instructors configuring algorithmic tools while at the same time supporting student agency and learning about teamwork.more » « less
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