Peer review #1

Pathways Lab is becoming Pathways Network: linking people nationally and worldwide who are building a cumulative science of education/learning sequences. 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!

Peer review #2

Pathways Lab is becoming Pathways Network: linking people nationally and worldwide who are building a cumulative science of education/learning sequences. 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!

Classifying courses at scale: A computational approach to understanding student course-taking in administrative transcripts

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.

Understanding academic pathways through course engagement

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.

Measuring curricular breadth in institutional context

Among the characteristics of a liberal arts education, curricular breadth stands out as being among the most often-cited and frequently prescribed components, while simultaneously being among the least well-researched aspects of student learning. Breadth is articulated as meeting general education and distribution requirements at the institutional level, with graduation serving as a marker that sufficient breadth has been achieved. How breadth is empirically realized by students operating within their academic programs is an open question given that common measures of curricular breadth do not exist. In this project, we measure undergraduate curricular breadth based on the body of coursework undertaken by students using individual-level transcript data. The breadth metrics we calculate will be of interest to researchers interested in probing the association between the “dosage” of breadth in the curriculum and variation in educational and life outcomes. We also offer thoughts about the utility of such measures and whether it is really possible for a concept as complex and amorphous as curricular breadth to be measured using indices that are, by their nature, reductive.

Quantifying complexity: Trying to measure curricular rules

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.

“Can someone explain how we TAG, again?” Keystone agents and curriculum navigation in community college transfer pathways

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.

Segregation, ethnic disparities in university application choices, and educational stratification: Evidence from revealed choice data

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.

Making Sense of Curved Grades

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.

Framing a science of educational progress

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.

Connecting academic pathways to career outcomes

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.

Collaborating in class: Social class context and peer help-seeking and help-giving in an elite engineering school

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.

College major restrictions and educational efficiency

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.

Initial Results from a Decade-Spanning Longitudinal Study on the Curricular Complexity of Engineering Programs

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.

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.

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.

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.