Misconceiving merit: Paradoxes of excellence and devotion in academic science and engineering 

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.

From transcripts to trajectories: A data-driven framework for studying academic pathways

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.

Defining and measuring task complexity in major requirements

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 trouble with passion: How searching for fulfillment at work fosters inequality

“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.

Credit hours is not enough: Explaining undergraduate perceptions of course workload using LMS records

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.

Kickoff: Motivation for the seminar and flash introductions

At this inaugural session, Mitchell offers a brief history of the Stanford Pathways Lab and the ambitions of the seminar for the coming year. Attendees should be prepared to offer a 2-3 sentence introduction to themselves and their work (no slides, please).

We also will huddle about setting norms for the seminar.

From pipelines to pathways in the study of academic progress

Much research on undergraduate education speaks of pipelines, but that metaphor is suboptimal for exploiting scaled data and elides the complexity of academic progress. We integrate insights from multiple scientific domains to specify a heuristic of pathways that better fits both the phenomenon and available empirical material.

This paper defines academic pathways as joint outcomes of curricular programs that variably provide course options, and sequences of considered and selected academic opportunities. Pathways can be enabled, inhibited, or prevented by institutions; and taken, avoided, and forged by students. With investments in data infrastructure, a coherent science of academic pathways promises new inquiries and strategies to improve student persistence, timely degree completion, equity and inclusion in higher education.

Observing undergraduate pathways at close range

Since Summer 2019, our team has been interviewing a panel of approximately 80 undergraduates as they navigate Stanford University. We interview these students three times a year near the drop/add period of each academic term, generating fine-grained data about academic decision-making and identity development. This presentation provides an overview of the scientific ambitions of our study, seeking collegial input on our research priorities for the coming year.

Major requirements, peer composition, grading practices and student course trajectories

UC-Irvine’s undergraduate measurement project has collected unprecedented data on student experiences, trajectories and outcomes. The data include administrative records, learning management system logs, longitudinal surveys, experiential sampling responses and performance assessments. UCI researchers will focus this session on using some of that data to explore how major requirements, peer composition and grading practices are associated with student pathways.