Status: Past

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.

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.”