Status: Past

Scholars have extensively documented social class differences in students’ relationships with educational institutions through their interactions with authority figures and the unequal institutional advantages these interactions yield. However, little is known about whether or how social class also shapes students’ peer interactions in ways that produce these inequalities. Using a qualitative case study of an elite engineering school in which I draw on participant observation and interviews with 88 undergraduates and six administrators, I argue that social class context—a proxy for social class—shapes the peer help-seeking and help-giving (collaborative) strategies students use, which can create inequalities in the institutional advantages they secure in the form of academic help, support, and learning opportunities. Focusing specifically on the social class context of students’ high schools, I find that compared to their less-privileged counterparts, privileged students—who came from class-advantaged high school contexts where they became familiar with collaboration and upper-middle-class cultural signals—more easily collaborated with their college classmates and displayed signals that communicated they were “good” collaborators. The findings highlight new mechanisms through which inequalities are reproduced in educational institutions and make theoretical contributions to research on cultural capital, inequality, and education. The results also have implications for group performance and the use of collaborative learning as an instructional method. 

 This talk is based on my recent article by the same title.

Over half of students at R1 public universities – and over three-quarters of students in lucrative majors like engineering and economics – earn college majors that impose GPA or application restrictions on which students are permitted to declare the major. A typical restriction prohibits students who earn lower than B or B- grades in the department’s introductory courses from declaring the major. Our prior work has shown that major restrictions differentially impact disadvantaged students and lead them toward lower-value college majors. This study investigates six potential efficiency benefits and costs of major restriction policies: e.g. whether restrictions differentially admit students with comparative advantages in the field, whether restrictions push low-GPA students into fields of study in which they are more likely to graduate, and whether restrictions increase college majors’ value to their remaining students. We find no evidence of efficiency benefits and substantial evidence of efficiency costs of major restriction policies relative to not implementing major restrictions.

I will present preliminary analyses from a longitudinal study that supplements the Multiple Institution Database for Engineering Longitudinal Development (MIDFIELD), a comprehensive dataset providing valuable information about how diverse engineering students have performed and been represented across disciplines since the 1980s, with new curricular data. The study focuses on characterizing the role of the curriculum in perpetuating systemic barriers to degree progress for underrepresented groups in engineering by understanding which curricular design patterns best support degree completion and analyzing student course-taking behavior when contextualized with the codified plan of study. We sampled plans of study from 13 institutions in Mechanical, Electrical, Chemical, Civil, and Industrial Engineering, starting with the most recent catalog year for the institution in MIDFIELD and looking back ten years, resulting in 515 plans of study. We processed the data using Curricular Analytics, a method of assigning values to curricular arrangements and measuring a plan of study’s complexity using network analysis, and have conducted preliminary analyses using descriptive statistics, boxplots, and trends plotted by catalog year.

Credit hours traditionally quantify expected instructional time per week in a course, informing student course selection decisions and contributing to degree requirement satisfaction. In this study, we investigate course load measures beyond this metric, including determinants from course assignment structure and LMS interactions. Collecting 596 course load ratings on time load, mental effort, and psychological stress, we investigate to what extent course design decisions gleaned from LMS data explain students’ perception of course load. We find that credit hours alone explain little variance compared to LMS features, specifically number of assignments and course drop ratios late in the semester. Student-level features (e.g., satisfied prerequisites and course GPA) exhibited stronger associations with course load than the credit hours of a course; however, they added only little explained variance when combined with LMS features. We analyze students’ perceived importance and manageability of course load dimensions and argue in favor of adopting a construct of course load more holistic than credit hours.

The talk will cover a recent paper by the same title as well as touch on related work, past and in-press.

This study examines the extracurricular choices of first year students at Western University and finds disparities in the level of involvement and types of extracurricular participation by student demographic. Racially/ethnically underrepresented women participate in more extracurricular organizations and for more quarters than their peers. They participate in higher concentrations in almost every type of organization except paid work, research, and academic extension activities. I will consider implications of these findings for academic and professional outcomes and add to the literature on racialized time and what sociologist Erin Cech calls “choicewashing.”

How is it that academic STEM, which reveres meritocracy, produces outcomes in which women, LGBTQ individuals, and some racial minority academics are systematically underrepresented and devalued?  In contrast to the common focus on implicit bias, Cech and Blair-Loy examine the cultural foundations of academic STEM.  Although academic scientists today view implicit bias as distorting academic judgement, most STEM faculty venerate the core cultural content of academic STEM.  The authors define this core cultural content as a set of “cultural schemas,” historically rooted, broadly-shared understandings of merit that shape cognition, emotion, and moral commitments.

The “schema of scientific excellence” highlights the qualities of individual brilliance and assertive self-promotion. The “work devotion schema” demands single-minded allegiance of STEM faculty to the scientific vocation and delegitimates faculty with commitments to caregiving. When these schemas are used as yardsticks, they mis-measure merit. This talk summarizes the main points of a book by the same title, based on a multi-method case study at one R1 university.

“Follow your passion” is a popular mantra for career decision-making in the United States. In this talk, I will discuss research from my recent book,The Trouble with Passion, on this ubiquitous cultural narrative. This “passion principle” is rooted in tensions between postindustrial capitalism and cultural norms of self-expression and is compelling to college-educated career aspirants and workers because passion is presumed to motivate the hard work required for success while providing opportunities for meaning and self-expression. Although passion-seeking seems like a promising option for individuals hoping to avoid drudgery in their labor force participation, I argue that the passion principle has a dark side: it reinforces socio-economic disadvantages and occupational inequality among career aspirants and workers in the aggregate and helps reproduce an exploited, overworked white collar labor force. These findings have implications for cultural notions of “good work” popular in higher education and the workforce and raises broader questions about what it means when becoming a dedicated labor force participant feels like an act of self-fulfillment.

Graduating from college requires understanding major curricular requirements and making several complex interdependent choices to fulfill them. In this paper, we create measures to describe and quantify complexity in major requirements. We then compare complexity across disciplines and universities. We find wide variation in our measures of complexity within and across departments and campuses. To assess how well our measures of complexity match students’ experiences, we perform a laboratory experiment on student course-planning. Students in our experiment were 20 percentage points more likely to graduate with the least-complex set of requirements than the most-complex. Creating universal and broadly applicable measures of complexity gives policy makers and administrators better models for simplification, which could lead to meaningful and effective policy reforms.

The growing availability of digitized transcript data holds great promise for understanding students’ pathways through a college curriculum, revealing insight not just into the structure of academic curricula but also how students’ course-taking decisions navigate that structure. However, there are no widely established modeling approaches to reveal those pathways and assess how they differ among demographically distinct student groups. One challenge in using transcript data to study pathways is that the course-taking space is prohibitively large—over 4,000 classes at a large university—while the actual number of courses taken by any given student is comparatively tiny (~ 40). Additionally, raw transcript data does not reveal which course-taking sequences are indicative of a particular academic trajectory.

We present a conceptually appealing, data-driven framework for translating transcript data into information on students’ pathways. Our framework delivers information about students’ movements both through the space of possible majors and also within a particular program. This information is remarkably detailed, but this richness creates statistical challenges in that the analyst must allow for temporal dynamics, heterogeneity, and the possibility that students from a given demographic background may have distinct experiences in different majors. Thus we develop a multilevel statistical model that can leverage the richness of these data, with each level tuned to nonparametrically extract a different kind of substantive information about trajectories, student demographics, and major types, as well as how these interrelate.

We apply the model to reveal the diverse pathways students take within majors, and show how this analysis produces novel insights into differential experiences across gender, ethnic group, and economic background in STEM versus non-STEM fields.

Broad access institutions serve a key role in providing education to non-traditional students. We introduce findings from the first year of a five-year Gates Foundation Grant at Western Governors University that aims to identify and solve equity gaps in access, attainment, and outcomes at institutions in collaboration with external academic researchers. This agenda will focus on how institutions can better operationalize academic transcript data from students’ current and prior institutions to inform course planning and articulation agreements. We also explore how Natural Language Processing can act as augmented intelligence for college counselors and early warning systems. Lastly, we consider how this work can be used to construct a virtuous cycle of qualitative and quantitative work informing one another for institutional improvement.