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Understanding college student career exploration, preparation and job search behavior in relationship to college-career trajectories has been hampered by a dearth of observational data. At The University of California – Irvine, we found a way to bridge this gap. We leverage detailed student-level information from the Handshake career services platform to examine student career exploration, preparation, advising on internships, job searches, employer campus visits, career fairs, and job applications, and link these data with students’ administrative, learning management systems and survey data to capture student career development in college and its impact on initial forays into the labor market. The presentation will outline the project motivation, scope, data, and initial findings.

In a landscape where the gap between the need for skilled talent and the availability of job opportunities is broad, Year Up stands out as a transformative force for economic mobility. For over two decades, this national nonprofit organization has served over 43,000 young adults—90% of whom identify as persons of color with training and opportunity— to connect to jobs that offer livable wages and opportunities for growth. Through an overview of Year Up’s innovative program model, this presentation will illustrate how the organization has achieved the highest wage gains among sectoral training programs as evidenced by robust evaluations setting a benchmark for success in job training and employment support. Motley also will discuss Year Up’s future directions, including its commitment to expanding its reach and deepening its impact through research in industry to ensure that our work is what employers are demanding. Many scholars and researchers are wondering what the most important components of sectoral trainings are to replicate in other settings. Year Up does not believe the answer is simple (i.e. we need technical skills training, wrap around student supports, and coaching); however, it does contend the piece that really differentiates its program is high-quality job placement.

Large introductory lecture courses are frequently post-secondary students’ first formal interaction with science, technology, engineering, and mathematics (STEM) disciplines. Grade outcomes in these courses are often disparate across student populations, which, in turn, has implications for student retention. This study positions such disparities as a manifestation of systemic inequities along the dimensions of sex, race/ethnicity, income, and first-generation status and investigates the extent to which they are similar across peer institutions.

Duke University sociologist Jessi Streib provides an overview of her latest book, The Accidental Equalizer: How Luck Determines Pay after College (Chicago, 2023).

Hayward will present a demonstration of the Program Pathways Mapper, a tool for clarifying curricular pathways to transfer and completion, along with qualitative data from student and staff focus groups and quantitative data showing the impact of the program mapper on course selection at the initial institution (Bakersfield College). A difference-in-difference analysis of the student success metrics of the first twelve California colleges to implement the program mapper relative to 89 that had not yet begun implementation will also be presented as evidence that the program mapper is capable of “moving the needle” on important institutional outcomes.

Social stratification arises from disparities in both the range of opportunities available to people and in how people navigate those opportunities. Yet the relative influences of these two dimensions are rarely observed, as most available information is limited to realized actions. We use the case of university admissions in Sweden to overcome this observational problem. Leveraging comprehensive register and archival data, we break down the cumulative impact of access and navigation differences on the production of gender and social background stratification in university programs of study with varying social outcomes. We show that: (1) In the aggregate, social background and gender exhibit significant cumulative and non-interactive influence on the array of attainable programs, favoring women and individuals from higher social-class backgrounds (2) There is a strong and interactive effect of gender and parents’ SES on the distribution of outcomes of reachable programs (2a) an increase in SES quintile has a systematically positive effect on the median social outcome for each gender, but the spread of this effect between the first and last quintile is twice larger among men than among women (2b) While there is a very large difference between the median social outcome of lower SES quintile of men and women, in favor of women, there is no difference between men and women of the highest SES quintile (3) The number of reachable programs explain most of the difference between applicants and non-applicants, as well as the difference between successful and non successful applicants (4) Net of the space of possible outcome, men of all SES quintiles navigate their possibilities towards higher social outcomes programs than women of all SES, with little to no secondary effect of SES, except for women of the highest quintile outperforming females from the lowest ones in the navigation of their possibles.

Dandaneau and Heileman will provide an overview of the curricular analytics research program under the auspices of the Association for Undergraduate Education at Research Universities (UERU). The organization recently received a major grant from the Ascendium Educational Group for study of how curricular structures can inform faculty curriculum oversight and, through intentional data-guided structural reform, facilitate student learning and equitable student success.

Reichlin’s firm, CollegeApp, combined over 200,000 survey responses with commercially-available demographic, financial, employment, and lifestyle data on 241 million US adults.  CollegeApp used machine learning to score each US adult on their intent to enroll in higher education and training; their preference for which type of institution to attend; their preference for instructional delivery mode; and their motivations for enrolling.  Based on this research, Reichlin’s talk explores the geography of who intends to enroll in higher education and training programs.

Despite billions of dollars invested in STEM education and employer claims of shortages of STEM graduates, only about a third of STEM graduates work in STEM jobs. This talk explores the reasons why, and how returns on STEM education can be improved. It offers an overview of Skrentny’s new book, Wasted Education: How We Fail Our Graduates in Science, Technology, Education and Math (Chicago, 2023).

This project utilizes course enrollment data from Cornell University to assess how students from different social groups (self-identified race/ethnicity, gender, first gen, and domestic vs. international status) are segregated across courses, how much of this segregation is tied to college major, and whether students are more likely to swap out of courses where they are social isolates.

Racial and ethnic disparities in educational trajectories and outcomes continue to be central concerns for stratification scholars and policymakers worldwide. A key contributor to these disparities lies in ethnic and racial variations in college application behaviors, which lead to higher rates of academic mismatch among disadvantaged applicants. This paper delves deeper into the role of decision-making processes in generating ethnic and racial disparities in college application choices. We propose that application considerations anchored in an unequal and segregated opportunity structure can generate systematic group differences in college application choices, resulting in suboptimal outcomes for disadvantaged minorities. We evaluate this argument using unique administrative records detailing the revealed choices of Jewish and Arab applicants to universities in Israel, recognizing the high levels of ethnic segregation, education, and labor market stratification in this country. The data and context allow us to pinpoint group differences in decision-making because we can discount costs, geographic proximity, or information constraints—factors often cited as reasons for disparities in application choices. Results from conditional logit (choice) models uncover ethnic differences in how applicants weigh program characteristics. This leads to substantial variation in the rate of academic mismatch and accounts for the bulk of the ethnic gap in university admission. Results demonstrate the importance of decision-making processes in understanding ethnic-racial stratification.

Community college (CC) students who intend to transfer to baccalaureate programs often encounter complex curricular requirements. To navigate them, students activate their social and academic networks in a variety of ways. In this case study of a cohort of CC students in an urban system, we trace the the importance of those we call keystone agents — people in network positions which bridge campus ecologies. We find that keystone agents are important source of information and other supports. We illustrate how keystone agents share information across student networks and how their beliefs about curriculum navigation hold sway over students’ course-taking behaviors, even when these beliefs run counter to the design of guided pathways programs and other local campus-based interventions. Keystone agents’ information sharing aims to create organizational pathways that are intended to reduce friction within CC course sequences, but they also have a series of unintended consequences when students choose to transfer. We offer implications for the development of transfer support programs and interventions, curricular policy-making, and the design of campus environments.

In this update of work presented at the Pathways seminar in February 2023, we will present our approach to creating measures to describe and quantify complexity in major curricular requirements, which may act as a barrier to the students’ ability to navigate college. We discuss our general goals in creating the measures, the past work we draw upon, and our different analytic approaches, which were variably fruitful. We present descriptive results showing our measures of task complexity in major requirements in four departments at each of 32 colleges.

Doing good pathways research requires access to the right kinds of data. For example, studying students’ trajectories through college requires data that is longitudinal and relational; learning from the diverse experiences of students at different types of institutions requires data from multiple colleges; and understanding the long-term value of educational experiences requires data that follows students well beyond college. It is rare for one data source to have all these qualities—plus be accessible to all qualified researchers—which is why we constructed the College and Beyond II (CBII) data. The purpose of CBII is to democratize access to rich institutional data, and in doing so, produce new insights about how undergraduate education works. In this presentation we provide a general overview of CBII that highlights the many data types (e.g., administrative records, transcripts, survey outcomes, written responses) and measures (e.g., validated scales, National Student Clearinghouse enrollment and awards records, AP test scores) that are available. To illustrate the data’s potential, we will highlight preliminary work using the data. The presentation will also be conversational, allowing pathways researchers an opportunity to discuss how the data could be used to answer their own research questions and further their research agendas.

While existing research on academic pathways has typically observed progress via observation of course enrollments and major selection, there are more subtle aspects of students’ everyday experiences that comprise academic progress as well. My research explores the potential of large-scale digital trace data from learning-management systems (such as Canvas) to capture students’ longitudinal patterns of engagement, which is a precondition for development and success in higher education. By examining engagement patterns, I provide a more nuanced and comprehensive picture of student activity and experience, and better understand the development of academic pathways.

Social scientists have long recognized that students’ course grades are consequential for academic progress, yet they have devoted little attention to variation in the protocols through which instructors assign grades. I call these protocols “grading practices.” Their variation may be especially wide in college settings, where instructors often have considerable discretion over grading practices. In some practices, grades are criterion-based, wherein student performance is compared against a set of standards. In other cases, students are compared to the performance of other students in a practice known as curving. Students entering higher education face the challenge of recognizing variation in grading practices and making sense of them under conditions they may perceive as high stakes. I report preliminary findings from a longitudinal study of undergraduates moving through an admissions-selective university to demonstrate the breadth of variation grading practices students encounter. I find substantial variation in how grades are assigned even among courses utilizing curved grades. Perhaps remarkably, initial analyses of qualitative interview data with students in courses with curved grades surface little evidence that grading curves per se engender competition; rather, perceptions of grades in curved courses are highly dependent on course structure and students’ previous exposure to course content.

Postsecondary course-taking is of interest to researchers from diverse domains including economics, sociology, and policy. Transformations in digital infrastructure mean researchers increasingly have access to rich administrative transcripts on course-taking. However, administrative transcripts are seldom standardized across institutions or state systems, preventing researchers from easily examining trends in course-taking and course pathways at scale. To address this challenge, we apply machine learning and natural-language processing techniques to efficiently standardize administrative transcripts at scale. Drawing on four waves of the National Center for Education Statistics’ Postsecondary Education Transcripts Studies, we train logistic regression models to classify courses drawn from administrative transcripts into the College Course Map, a hierarchical taxonomy of course-taking. We apply these models to administrative transcripts from 18 institutions in the College and Beyond II dataset and use the standardized transcript measures to examine longitudinal trends in course-taking in the core liberal arts and professional disciplines from ten years of cohorts of baccalaureate graduates. Contrasting these trends in course-taking with those of majors, we find that the proportion of course enrollments in the core liberal arts is meaningfully higher than that of the proportion of majors in those fields. Examining course-taking trends within major, we descriptively observe that majors in three of the core liberal arts domains – the natural sciences, humanities, and social sciences – take substantially more of their coursework outside of their home discipline but within the liberal arts than majors in the professional disciplines and fine arts.

This is the first of two opportunities for peer review of a working draft of the Pathways Network website. We want the site to reflect your own ideas and ambitions. Please be ready to give critical feedback on a website that we hope will bear your name!

This is the second of two opportunities for peer review of a working draft of the Pathways Network website. We want the site to reflect your own ideas and ambitions. Please be ready to give critical feedback on a website that we hope will bear your name!

Educational phenomena are sequential, cumulative, and contingent, but educational social scientists have only rarely modeled their inquiries to capture this complexity. Newly available computational tools and scaled data make it possible to observe the sequential, cumulative, and contingent character of educational progress at micro, meso, and macro levels. This session is our latest effort to integrate work from a range of fields to develop heuristics for a new science of educational progress. Our goal is theoretical and methodological pluralism through conscientious matching of inquiry design, data and substantive problem.

Students and their parents hold strong convictions about how certain academic choices will affect their competitiveness on the labor market upon graduation. These beliefs influence students’ academic choices, typically in ways that increase their workload, such as taking on additional majors, minors, or challenging courses. Despite their significant impact on students’ college experiences, these beliefs are rarely grounded in evidence. This research project tests the evidentiary basis of some of the most pervasive beliefs and investigates which academic choices have been most influential for several different career outcomes. We use ten years of individual-level academic and career data at a public Land grant university in the United States. We will discuss implications for student advising, curriculum design, and persistence and equity.

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.

“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.

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.”

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.

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.

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.

The presentation originally scheduled for 21 November has been postponed due to illness. Instead we will continue the rich dialogue begun by the UC-Irvine team on 10 November. As a working definition, let’s say that grading regimes are systems whereby official scores and grades are assigned to academic coursework. Grading regimes include such things as: letter-grade vs. pass/fail grades; curved grading systems; intramural variation in grade distributions; student cultures around the meaning of grades; and minimum grade or GPA requirements as criteria for access to specific programs.

The forum will consist of “flash” presentations (five minutes max) of completed scholarship or work in progress on this broad topic. If you would like to offer a flash presentation, please write to Mitchell at stevens4@stanford.edu. First come, first served.

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.

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.

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.

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.