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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. 

As part of a larger project to assess what marginalization looks like in engineering student teams in the classroom, an opportunity evolved to measure gender and race/ethnicity more authentically and more safely than is commonly done. This paper describes the design of these authentic questions and how students responded to them. In the case of the race/ethnicity question, the paper compares student responses to the new question to their responses to an earlier question that had no option to select multiple identities and no opportunity to write in a free-text response. This process makes visible the students who were likely harmed by the old question design, emphasizing the importance of an authentic measurement. 

Multiple stakeholders are interested in measuring undergraduate student success in college across academic fields. Different metrics might appeal to different stakeholders. Some metrics such as the fraction of first-time, full-time students who start in the fall who graduate within six years, the graduation rate, are federally mandated by the U.S. Department of Education, Integrated Postsecondary Education Data System (IPEDS). We argue that this calculation of graduation rate is inherently problematic because it excludes up to 60% of students who transfer into an institution, enroll part-time, or enroll in terms other than the fall. By expanding the starters definition, we propose a graduation rate definition that includes conventionally excluded students and provides information on progression in a specific program. Stickiness is an even more-inclusive alternative, measuring a program’s success in graduating all undergraduates ever enrolled in the program. In this work, programs are grouped into six academic fields: Arts and Humanities, Business, Engineering, Other, Social Sciences, and STM (Science, Technology, and Mathematics. Stickiness is the percentage of students who ever enroll in an academic field that graduate in the same field. We use the Multiple Institution Dataset for Investigating Engineering Longitudinal Development (MIDFIELD) 2023 which contains unit-record data for over 2 million individual students at 19 institutions. For the academic fields studied, Engineering has the highest graduation rate and third highest stickiness. Social Sciences and Business also have higher graduation rates and stickiness than the other fields. We also track the relative fraction of students migrating to and from each academic field. This paper continues our work to derive better metrics for understanding student success. 

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. 

In this paper, we discuss the results from our study on the experiences of first-year Black and Brown engineering students in engineering teams. This work is part of ongoing research on identifying teams engaging in marginalizing behaviors against minoritized (race, gender, LGBTQ identity, nationality) students. Using a diary study methodology, we explore the team experiences of Black and Brown students by examining two research questions: 1) what does racial marginalization look like within engineering classrooms where teamwork is a primary feature and 2) what experiences from the dairies inform researchers and faculty about participants’ experiences and personal knowledge of how race and racism operates in teams. We identified two central themes: 1) participants often avoided conversations when race could be a potential topic, and 2) participants believed that racism was a normal part of teams (in both the classroom and workplace). Participants explained that even if race was not explicitly discussed during their group work, they sensed that implicit bias and discrimination were affecting their experience. Further, when we asked participants how to increase support related to their teaming experience, they reported feeling unsure of what can be done to eliminate behaviors of racism and marginalization from engineering education. The participants expressed that change needed to happen so that other Black and Brown students are welcomed into the field, but that no one on campus (peers, faculty, and staff) has asked them about ideas for change. This diary study provides important insights into how Black and Brown engineering students contextualize their experience with marginalizing behaviors in teams. 

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.