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Theses Doctoral

Equity and Higher Education: Essays on Performance-based Financial Aid, Community College Degree Completion, and Dual Enrollment

Yanagiura, Takeshi

This paper consists of three independent, quantitative studies on three higher education policy issues in the U.S. : 1) Performance-based Financial Aid, 2) Community College Degree Attainment, and 3) Dual Enrollment. The first essay discusses how low-income students in free college programs respond to strict achievement standards exceeding the minimum requirement for federal financial aid. To address this question, I examined the impact of a new credit completion requirement for Indiana’s statewide free college program. This program is only available for low-income students and recently increased the number of credits required for maintaining eligibility from “taking” 24 credits per year to “earning” 30 credits per year in 2013. Using Indiana’s statewide administrative data on college students, I exploit the sudden change in the eligibility renewal rule to identify the causal effects of the requirement on their postsecondary outcomes. I found that the new rule increased the likelihood of completing a bachelor’s degree within four years by 2.5 percentage points. At two-year institutions, the policy had mixed impacts, increasing the chance of graduation within two years by 2.9 percentage points but also lowered the second-year persistence rate by 3.7 percentage points. Meanwhile, the number of of degree completers within five years remained unchanged in both the sectors, suggesting that strict achievement requirements only improve program efficiency but not overall productivity in terms of degree attainment. Lastly, the policy effects are largely driven by community college students and students whose high school GPA is at or below the median. This implies that schooling decisions that the policy is intended to influence are mostly concentrated among those students.

In the second essay, I discuss how well machine learning (ML) techniques predict the chance of postsecondary credential attainment for students who started at community colleges. Among community college leaders and others interested in reforms to improve student success, there is growing interest in adopting ML techniques to predict credential completion. However, ML algorithms are often complex and are not readily accessible to practitioners for whom a simpler set of near-term measures may serve as sufficient predictors. This study compares the out-of-sample predictive power of early momentum metrics (EMMs)—13 near-term success measures suggested by the literature - with that of metrics from ML-based models that employ approximately 500 predictors for community college credential completion. Using transcript data from approximately 50,000 students at more than 30 community colleges in two states, I find that the EMMs that were modeled by logistic regression accurately predict completion for approximately 80% of students. This classification performance is comparable to that of the ML-based models. The EMMs even outperform the ML-based models in its ability to approximate the actual probability of degree completion. These findings suggest that EMMs are useful predictors for credential completion and that the marginal gain from using an ML-based model over EMMs is small for credential completion prediction when additional predictors do not have strong rationales to be included in an ML-based model, no matter how large the number of those predictors may be.

The third essay focuses on dual enrollment programs at community colleges. The number of high students taking college courses has grown dramatically over the past two decades but little is known about their long-term educational outcomes. Using student-level data obtained from the National Student Clearinghouse, this study provides state-level descriptive analyses on the demographic characteristics of dual-enrolled students, as well as their educational attainment statuses in their early 20s. We tracked more than 200,000 high school students who first took a community college course in fall 2010 for six years, through summer 2016 (five years after high school). Eighty-eight percent of these students continued in college after high school, and most earned a certificate or degree or transferred from a two-year college to a four-year college within five years. What type of college former dual enrollment students attended after high school and how many completed a college credential varied greatly by state, and many states showed big disparities in credential completion rates between lower and higher income students.

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More About This Work

Academic Units
Economics and Education
Thesis Advisors
Matsudaira, Jordan
Ph.D., Columbia University
Published Here
July 23, 2020