Rachel Baker

Persistence Patterns in Massive Open Online Courses (MOOCs)

Using a unique dataset of 44 Massive Open Online Courses (MOOCs), this article examines critical patterns of enrollment, engagement, persistence, and completion among students in online higher education. By leveraging fixed-effects specifications based on over 2.1 million student observations across more than 2,900 lectures, we analyzed engagement, persistence, and completion rates at the student, lecture, and course levels. We found compelling and consistent temporal patterns: across all courses, participation declines rapidly in the first week but subsequently flattens out in later weeks of the course. However, this decay is not entirely uniform. We also found that several student and lecturespecific traits were associated with student persistence and engagement. For example, the sequencing of a lecture within a batch of released videos as well as its title wording were related to student watching. We also saw consistent patterns in how student characteristics are associated with persistence and completion. Students were more likely to complete the course if they completed a pre-course survey or followed a quantitative track (as opposed to qualitative or auditing track) when available. These findings suggest potential course design changes that are likely to increase engagement, persistence, and completion in this important, new educational setting.

The Effects of Structured Transfer Pathways in Community Colleges

Most of the students who set out to earn degrees in community colleges never do. Interventions that simplify the complex organizational structures of these schools are promising solutions to this problem. This article is the first to provide rigorous evidence of the effects of structured transfer programs, one such intervention. Leveraging the phased rollout of transfer programs in California, I find large effects of the policy on degrees earned in treated departments. In the first 2 years, this growth was not coupled with growth in total degrees granted or in transfers, but in the third year, there is evidence of increased transfer. The analyses also show that the policy could affect equity; departments that offer transfer degrees became more popular and there is suggestive evidence that the highest achieving student groups enrolled in these classes at higher rates.

A Randomized Experiment Testing the Efficacy of a Scheduling Nudge in a Massive Open Online Course (MOOC)

An increasing number of students are taking classes offered online through open-access platforms; however, the vast majority of students who start these classes do not finish. The incongruence of student intentions and subsequent engagement suggests that self-control is a major contributor to this stark lack of persistence. This study presents the results of a large-scale field experiment (N = 18,043) that examines the effects of a self-directed scheduling nudge designed to promote student persistence in a massive open online course. We find that random assignment to treatment had no effects on near-term engagement and weakly significant negative effects on longer-term course engagement, persistence, and performance. Interestingly, these negative effects are highly concentrated in two groups of students: those who registered close to the first day of class and those with .edu e-mail addresses. We consider several explanations for these findings and conclude that theoretically motivated interventions may interact with the diverse motivations of individual students in possibly unintended ways.

Agent-Based Simulation Models of the College Sorting Process

We explore how dynamic processes related to socioeconomic inequality operate to sort students into, and create stratification among, colleges. We use an agent-based model to simulate a stylized version of this sorting processes in order to explore how factors related to family resources might influence college application choices and college enrollment. We include two types of “agents†—students and colleges—to simulate a two-way matching process that iterates through three stages: application, admission, and enrollment. Within this model, we examine how five mechanisms linking students’ socioeconomic background to college sorting might influence socioeconomic stratification between colleges including relationships between student resources and: achievement; the quality of information used in the college selection process; the number of applications students submit; how students value college quality; and the students’ ability to enhance their apparent caliber. We find that the resources-achievement relationship explains much of the student sorting by resources but that other factors also have non-trivial influences.

Race and Stratification in College Enrollment Over Time

We measure college enrollment selectivity gaps by race-ethnicity using a novel method that is sensitive to both the level (2- vs. 4-year) and selectivity of the college in which students enroll. We find that overall Hispanic–White and Black–White enrollment selectivity gaps closed in the United States between 1986 and 2014. This overall closing of gaps appears to be related to the closing of high school graduation gaps. However, this contraction was driven almost entirely by students at the margin between no college and college enrolling in non-degree-granting programs. Among students who enrolled in degree-granting schools, Black students have enrolled at increasingly less selective institutions than White students, whereas Hispanic–White gaps remained relatively unchanged over the nearly 30 years of our study. These gaps are concerning because of their implications for long-term economic inequality.

The different relationships between engagement and outcomes across participant subgroups in Massive Open Online Courses

Abstract Previous research has found that early engagement in MOOCs (e.g., watching lectures, contributing to discussion forums, and submitting assignments) can be used to predict course completion and course grade, which may help instructors and administrators to identify at-risk participants and to target interventions. However, most of these analyses have only focused on the average relationships between engagement and achievement, which may mask important heterogeneity among participant subgroups in MOOCs. This study examines how the relationship between engagement and achievement may vary across the four common behaviorally identified participant subgroups (“disengagers,” “auditors,” “quiz-takers,” and “all-rounders”) in three MOOC courses offered on the Coursera platform. For each of these subgroups, we used measures of behavioral and cognitive engagement from the first half of the ten-week courses to predict two outcomes: course grade and overall lecture coverage. Results indicate that the same engagement measure may be oppositely associated with achievement for different subgroups and that some engagement measures predict achievement for one subgroup but not another. These findings provide insight into both the benefits and the complexity of studying patterns of engagement from behavioral data and provide suggestions on the improvement of identification of at-risk participants in MOOCs.