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The persistence of engineering students through graduation continues to be a concern in higher education. Previous studies have highlighted a link between students' performance in introductory mathematics courses and graduation rates. Focusing on a crucial foundational course within the engineering curriculum, the purpose of this study is to investigate how students’ performance in Calculus I impact their persistence in the engineering program. Utilizing data from 22 diverse educational institutions using Multiple-Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD), multilevel discrete-time survival analysis is employed to examine the longitudinal persistence patterns among the nested structure of students within institutions. Discrete-time analysis is an event-based approach that has the advantage of analyzing time in discrete chunks during which the event of interest could occur. The technique is a type of survival analysis, which has been used in other studies in engineering education and other educational studies. This approach addresses various challenges associated with analyzing student persistence data such as dealing with censored observations – observations for whom their entire educational pathway is not yet known because they are still enrolled. Using a multilevel form of this analysis approach also accounts for the hierarchical nature of the data involving students nested within institutions and incorporating variables that change over time. Thus, the study takes into account the variability and complexities inherent in the analysis of different institutions and examines persistence patterns more comprehensively than previous studies. By incorporating a diverse range of institutions, the study captures a broader spectrum of experiences and contexts, which enhances the generalizability of the results. 

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. 

According to the United States Bureau of Labor Statistics, union density amongst engineering workers within the US hovers around 7%. Despite hundreds of thousands of US engineers participating in the labor movement, engineering education on labor unions has been virtually non-existent within US higher education engineering programs. US higher education engineering programs are critical junctures in the making of engineers that have long histories of ensnarement by corporate industries with vested interests in undermining organized labor. This stark and significant absence of labor education coupled with decades-long denunciations that many engineering professional societies have made to discourage participation of engineers in building labor unions and the labor movement interrupt engineers’ capacity to collectively leverage our power for safer, healthier, and more just workplaces and worlds. An imperative task in the (re)development of the US engineering workforce is to build and strengthen union density amongst engineers by expanding unionization pathways. This paper offers a preliminary report back on a broader engineering workforce development project to nurture relationships between an unorganized (i.e. non-union) engineering research center and organized labor. Herein, we uplift stories from union members describing their pathways from higher education engineering programs to labor unions. Group interview conversations illuminating these stories offer broader contextualization for the sparseness and rarity of the paths from engineering programs to labor unions. Dialogue from group interviews further pointed toward opportunities to expand unionization pathways for engineering workers. 

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. 

The Multiple Institution Database for Investigating Engineering Longitudinal Development (MIDFIELD) has been developed over many years with substantial investment by the National Science Foundation through Engineering Education and Centers in the Engineering Directorate and the Division of Undergraduate Education in the Education and Human Resources Directorate. This project is focused on transitioning MIDFIELD to the American Society for Engineering Education (ASEE). The current team of MIDFIELD researchers continues to support this project including helping others learn to use the database. We have developed detailed tutorials in R that introduce MIDFIELD, key metrics, and example scenarios. We have also designed and facilitated workshops. In year 2, we offered the MIDFIELD Institute, an online three-day workshop to help researchers learn about and use MIDFIELD effectively. Attendees included graduate students, early career faculty, senior faculty, and an NSF program officer. Results from the 2023 offering of the MIDFIELD Institute are described in this paper. Dissemination and products are also summarized. 

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.