Drawing on their own design for a longitudinal cohort study of undergraduate students, Harrison and Hernandez share techniques for smoothing research relationships and logistics through widely available digital media.

Elective curriculums require undergraduates to choose from a large roster of courses for enrollment each term. It has proven difficult to characterize this fateful choice process because it remains largely unobserved. Using digital trace data to observe this process at scale at a private research university, together with qualitative student interviews, we provide a novel empirical study of course consideration as an important component of course selection. Clickstream logs from a course exploration platform used by most undergraduates at the case university reveal that students consider on average nine courses for enrollment for their first fall term (<2% of available courses) and these courses predict which academic major students declare two years later. Twenty-nine interviews confirm that students experience consideration as complex and reveal variation in consideration strategies that may influence how consideration unfolds. Consideration presents a promising site for intervention in problems of equity, career funneling, and college completion.

Analyzing 60,000 undergraduate applications to the University of California, the authors find that extant gender segregation of academic disciplines also manifests in intended major choice. Gender and SAT Math scores together strongly predict intent to major in biology and engineering, the most popular and gender-segregated majors. Using natural language processing, the authors also find that author gender is more predictive of essay topics written by prospective engineers than prospective biologists. Women intending to major in engineering write about essay topics that signal their gender identity to a greater degree than women intending to major in biology, perhaps to mitigate gender-transgressive academic commitments.

US colleges and universities are defined by their exclusivity, and the most prestigious schools reject most of those who apply. Yet these same schools also widely advertise their inclusiveness, encouraging students from all backgrounds to submit applications and highlighting evaluation protocols that identify many characteristics worthy of consideration for admission. We surface this paradox and use it as motivation to theorize a little studied component of college applications: personal essays.

This study considers the role of religious habitus and self-concept in educational stratification. The authors follow 3,238 adolescents for 13 years by linking the National Study of Youth and Religion to the National Student Clearinghouse. Survey data reveal that girls with a Jewish upbringing have two distinct postsecondary patterns compared to girls with a non-Jewish upbringing, even after controlling for social origins: (1) they are 23 percentage points more likely to graduate college, and (2) they graduate from much more selective colleges. They also analyze 107 interviews with 33 girls from comparable social origins interviewed repeatedly between adolescence and emerging adulthood to develop fuller portraits of how these patterns unfold.

Drawing on serial interviews (N = 200) of 53 students at an admissions-selective university, we show that incoming students with disparate precollege experiences differ in their orientations toward and strategies for considering first college math courses. Content repeaters opt for courses that repeat material covered in prior coursework, whereas novices opt for courses covering material new to them. Content repeaters receive high grades and report confidence in their math ability, whereas novices in the same classes receive lower grades and report invidious comparisons with classmates.

Supported with funds from the National Science Foundation, Stanford University hosted a virtual convening in July 2021 to frame an applied science to support working learners. The goal of this science is to measurably improve educational opportunities and mechanisms of occupational mobility for adult Americans. We forward nine recommendations.

Horwitz and Stevens synthesize several scholarly literatures to call for investments in early childhood education and flexible alternatives to four-year college degrees. Together these investments enable serial career transitions and meaningful lifelong learning.

We utilize a corpus of 240,000 admissions essays submitted by 60,000 applicants to the University of California in November 2016 to measure the relationship between the content of application essays, reported household income, and standardized test scores (SAT) at scale. We find that essays have a stronger correlation to reported household income than SAT scores.

As low-status academic offerings purveyed by high-status institutions, massively open online courses (MOOCs) are ambiguous credentials. In interviews with 60 people who devoted substantial time to at least one MOOC between 2014-2017, we find that people use MOOCs to build skills for application at work and home, build relationships, navigate life transitions, and enhance formal presentations of self.

Using digital trace data to observe this process at scale at a private research university, together with qualitative student interviews, we provide a novel empirical study of course consideration as part of the course selection process.

Is there a gender pay gap among graduates in some science, technology, engineering, and math (STEM) fields? Women and men have near-identical human capital at college exit, but cultural beliefs about men as more fit for STEM professions than women may lead to self-beliefs that affect pay. We hypothesized that women and men would be paid differently upon college exit, and that gender gaps in self-beliefs about one’s abilities, or self-efficacy, would correspond to this difference. Using data from a three-wave longitudinal study of graduates of engineering programs from 2015–2017, we find a confidence gap that aligns with a gender pay gap. Overall, these findings point to the role that cultural beliefs play in creating gender disparities among STEM degree-holders.

The US federal government has serially called upon colleges and universities to assist the nation in moments of national crisis. This brief outlines an ambitious plan to enlist the postsecondary sector in helping millions of Americans get back to work in the wake of the pandemic and equip them for ongoing prosperity in a highly dynamic economy.

The authors attended discussions and interviewed instructors in Stanford’s Computer Science Department to identify successful approaches and problem areas in the rapid transition to online learning.

College and university students submit millions of course reviews each year, yet these instruments are only rarely leveraged for scientific inquiry. This paper examines 11,255 reviews submitted to computer science courses to illustrate how such inquiry might proceed.

We show that a Reinforcement Learning (RL) model produces better learning gains using fewer educational activities than a linear assignment condition, and produces similar learning gains to a self-directed condition using fewer educational activities and with lower dropout rates.

We use a variety of text classification algorithms on a large corpus of college admissions essays to model the extent and ways that AI might inform human evaluation.

Intriguing relationships between video pacing, course persistence and academic achievement.

An appraisal of universities as distinctive institutions, on three dimensions.

Widely used word vector evaluation methods are biased towards the language of high income students. This problematically suggests that some students’ language is closer to the “ground truth” than others.

Using ten years of anonymized transcript data, the authors use a probabilistic modeling approach to model and predict student choices. This allows us to capture the complex relationships between courses, such as the tendency of some courses to serve as prerequisites for others without a formal requirement.

We offer an analytic toolkit, called Via, which transforms commonly available enrollment data into formal graphs that are amenable to interactive visualizations and computational exploration. The tool is intended for a variety of stakeholders: college students, instructors, and administrators.

Drawing on preliminary empirical research involving a web-based course exploration and planning tool in use at a private U.S. research university, we develop a conceptual framework for studying college students’ academic choices.

We conducted a large-scale field experiment in which all undergraduates were randomly encouraged to use Carta, a web-based course planning tool. We found that use of the platform lowered students’ GPA by 0.28 standard deviations on average.

Our study suggests new design goals for learning analytics systems, the importance of real-time analytics to many instructors, and the value of flexibility in data selection and aggregation for an instructor when working with an analytics system.

The higher education community must set the table and invite others to help us define ethical practice and responsible use of student data in the rapidly changing digital world of the academic enterprise.