Brian Heseung Kim

Heightened concerns about the health of the teaching profession highlight the importance of studying the early teacher pipeline. This exploratory, descriptive paper examines preservice teachers' (PST) expressed motivation for pursuing a teaching career and its relationship with PST characteristics and outcomes. Using data from one of the largest teacher education programs in Texas, we use a natural language processing algorithm to categorize into topical groups roughly 2,800 essay responses to the prompt, "Explain why you decided to become a teacher.'' We identify 11 topics that largely reflect altruistic and intrinsic (though not extrinsic) reasons for teaching. The frequency of motivation topics varied substantially by PST gender, race/ethnicity, and certification area. While topics collectively explained little of the variance in PST outcomes, we found preliminary evidence that intrinsic enjoyment of teaching and prior experiences with adversity predicted higher performance during clinical teaching and lower attrition as a full-time K–12 teacher.

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Inequality related to standardized tests in college admissions has long been a subject of discussion; less is known about inequality in non-standardized components of the college application. We analyzed extracurricular activity descriptions in 5,967,920 applications submitted through the Common Application platform. Using human-crafted keyword dictionaries combined with text-as-data (natural language processing) methods, we found that White, Asian American, high-SES, and private school students reported substantially more activities, more activities with top-level leadership roles, and more activities with distinctive accomplishments (e.g., honors, awards). Black, Latinx, Indigenous, and low-income students reported a similar proportion of activities with top-level leadership positions as other groups, although the absolute number was lower. Gaps also lessened for honors/awards when examining proportions, versus absolute number. Disparities decreased further when accounting for other applicant demographics, school fixed effects, and standardized test scores. However, salient differences related to race and class remain. Findings do not support a return to required standardized testing, nor do they necessarily support ending consideration of activities in admissions. We discuss reducing the number of activities that students report and increasing training for admissions staff as measures to strengthen holistic review.

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Despite decades and hundreds of billions of dollars of federal and state investment in policies to promote postsecondary educational attainment as a key lever for increasing the economic mobility of lower-income populations, research continues to show large and meaningful differences in the mid-career earnings of students from families in the bottom and top income quintiles. Prior research has not disentangled whether these disparities are due to differential sorting into colleges and majors, or due to barriers lower socioeconomic status (SES) graduates encounter during the college-to-career transition. Using linked individual-level higher education and Unemployment Insurance (UI) records for nearly a decade of students from the Virginia Community College System (VCCS), we compare the labor market outcomes of higher- and lower-SES community college graduates within the same college, program, and academic performance level. Our analyses show that, conditional on employment, lower-SES graduates earn nearly $500/quarter less than their higher-SES peers one year after graduation, relative to higher-SES graduate average of $10,846/quarter. The magnitude of this disparity persists through at least three years after graduation. Disparities are concentrated among non-Nursing programs, in which gaps persist seven years from graduation. Our results highlight the importance of greater focus on the college-to-career transition.

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Interactive, text message-based advising programs have become an increasingly common strategy to support college access and success for underrepresented student populations. Despite the proliferation of these programs, we know relatively little about how students engage in these text-based advising opportunities and whether that relates to stronger student outcomes – factors that could help explain why we’ve seen relatively mixed evidence about their efficacy to date. In this paper, we use data from a large-scale, two-way text advising experiment focused on improving college completion to explore variation in student engagement using nuanced interaction metrics and automated text analysis techniques (i.e., natural language processing). We then explore whether student engagement patterns are associated with key outcomes including persistence, GPA, credit accumulation, and degree completion. Our results reveal substantial variation in engagement measures across students, indicating the importance of analyzing engagement as a multi-dimensional construct. We moreover find that many of these nuanced engagement measures have strong correlations with student outcomes, even after controlling for student baseline characteristics and academic performance. Especially as virtual advising interventions proliferate across higher education institutions, we show the value of applying a more codified, comprehensive lens for examining student engagement in these programs and chart a path to potentially improving the efficacy of these programs in the future.

Open source code on GitHub.

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