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Benjamin L. Castleman

Di Xu, Kelli A. Bird, Michael Cooper, Benjamin L. Castleman.

Many public workforce training programs lead to industry-recognized, third-party awarded credentials, but little research has been conducted on the economic benefits of these credentials in the labor market. This paper provides quasi-experimental evidence on the labor market returns to industry-recognized credentials connected to community college workforce noncredit training programs. Based on novel data that includes approximately 24,000 working-age adults enrolled in noncredit workforce training programs at the Virginia Community College System, we employ a comparative individual-level fixed effects model to estimate earnings premia net of fixed attributes and earnings time-trends. Our results indicate that earning an industry-recognized credential, on average, increases quarterly earnings by approximately $1,000 and the probability of being employed by 2.4 percentage points, although there is substantial heterogeneity in economic return across different program fields. Back-of-the-envelope calculations suggest that the earnings gains associated with the industry credential obtained through the noncredit workforce training would exceed program costs in just over half a year on average.

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Kelli A. Bird, Benjamin L. Castleman, Yifeng Song, Renzhe Yu.

Colleges have increasingly turned to data science applications to improve student outcomes. One prominent application is to predict students’ risk of failing a course. In this paper, we investigate whether incorporating data from learning management systems (LMS)--which captures detailed information on students’ engagement in course activities--increases the accuracy of predicting student success beyond using just administrative data alone. We use data from the Virginia Community College System to build random forest models based on student type (new versus returning) and data source (administrative-only, LMS-only, or full data). We find that among returning college students, models that use administrative-only outperform models that use LMSonly. Combining the two types of data results in minimal increased accuracy. Among new students, LMS-only models outperform administrative-only models, and accuracy is significantly higher when both types of predictors are used. This pattern of results reflects the fact that community college administrative data contains little information about new students. Within the LMS data, we find that LMS data pertaining to students’ engagement during the first part of the course has the most predictive value.

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Benjamin L. Castleman, Denise Deutschlander, Gabrielle Lohner.

While Hispanic students represent the fasting-growing segment of the American school-age population, substantial gaps exist in college enrollment and Bachelor’s attainment between Hispanic and White and Asian students. Numerous factors contribute to these disparities and disproportionally affect Hispanic youth. In this paper, we contribute evidence on the impact of an intensive college advising program on Hispanic students’ college participation and degree attainment. We report on a multi-cohort randomized controlled trial of College Forward, which provides individualized advising from junior year of high school through college for a majority Hispanic, lower-income student population in Texas. Students who receive College Forward advising are 7.1 percentage points more likely to earn a Bachelor’s degree within 5 years of high school graduation; this effect appears largely driven by shifting high school graduates from the extensive margin of not going to college at all to instead enroll at four-year colleges and universities. Despite the costs associated with intensive advising programs like College Forward, back of the envelope calculations suggest that the benefit from increased college graduation induced by the program outweighs operating costs in less than three years following college completion.

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Kelli A. Bird, Benjamin L. Castleman.

Recent work highlights the challenge of scaling evidence-based educational programs. We report on a randomized controlled trial of a financial incentive program designed to increase the efficacy of a national remote college advising initiative for high-achieving students. We find substantial positive effects of the program on student engagement with college advisors; applications to well-matched colleges and universities; and review of financial aid awards. Yet treated students were no more likely to enroll at higher-quality institutions. Student survey responses suggest that institutional admissions and affordability barriers, alongside student preferences to attend institutions closer to home, explain the lack of enrollment effects.

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Kelli A. Bird, Benjamin L. Castleman, Yifeng Song.

Predictive analytics are increasingly pervasive in higher education. However, algorithmic bias has the potential to reinforce racial inequities in postsecondary success. We provide a comprehensive and translational investigation of algorithmic bias in two separate prediction models -- one predicting course completion, the second predicting degree completion. Our results show that algorithmic bias in both models could result in at-risk Black students receiving fewer success resources than White students at comparatively lower-risk of failure. We also find the magnitude of algorithmic bias to vary within the distribution of predicted success. With the degree completion model, the amount of bias is nearly four times higher when we define at-risk using the bottom decile than when we focus on students in the bottom half of predicted scores. Between the two models, the magnitude and pattern of bias and the efficacy of basic bias mitigation strategies differ meaningfully, emphasizing the contextual nature of algorithmic bias and attempts to mitigate it. Our results moreover suggest that algorithmic bias is due in part to currently-available administrative data being less useful at predicting Black student success compared with White student success, particularly for new students; this suggests that additional data collection efforts have the potential to mitigate bias.

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Suchitra Akmanchi, Kelli A. Bird, Benjamin L. Castleman.

Prediction algorithms are used across public policy domains to aid in the identification of at-risk individuals and guide service provision or resource allocation. While growing research has investigated concerns of algorithmic bias, much less research has compared algorithmically-driven targeting to the counterfactual: human prediction. We compare algorithmic and human predictions in the context of a national college advising program, focusing in particular on predicting high-achieving, lower-income students’ college enrollment quality. College advisors slightly outperform a prediction algorithm; however, greater advisor accuracy is concentrated among students with whom advisors had more interactions. The algorithm achieved similar accuracy among students lower in the distribution of interactions, despite advisors having substantially more information. We find no evidence that the advisors or algorithm exhibit bias against vulnerable populations. Our results suggest that, especially at scale, algorithms have the potential to provide efficient, accurate, and unbiased predictions to target scarce social services and resources.

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Brian Heseung Kim, Kelli A. Bird, Benjamin L. Castleman.

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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Andrew C. Barr, Kelli A. Bird, Benjamin L. Castleman, William L. Skimmyhorn.

Non-traditional students disproportionately enroll in institutions with weaker graduation and earnings outcomes. One hypothesis is that these students would have made different choices had they been provided with better information or supports during the decision-making process. We conducted a large-scale, multi-arm field experiment with the U.S. Army to investigate whether personalized information and the offer of advising assistance affect postsecondary choices and attainment among non-traditional adult populations. We provided U.S. Army service members transitioning out of the military with a package of research-based information and prompts, including quality and cost information on a personalized set of matched colleges, messages targeted at addressing veteran-specific concerns or needs, and reminders about key stages in the college and financial aid application process. For a randomly selected subset of the experimental sample, we also provided service members with opportunities to connect with a college advisor. We find no overall impact of the intervention on whether service members enroll in college, on the quality of their college enrollment, or on their persistence in college. We find suggestive evidence of a modest increase in degree completion within the period of observation, with these impacts mainly driven by increased attainment at for-profit institutions. Our results suggest that influencing non-traditional populations’ educational decisions and outcomes will require substantially more intensive programs and significant resources.

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Kelli A. Bird, Benjamin L. Castleman, Gabrielle Lohner.

The COVID-19 pandemic led to an abrupt shift from in-person to virtual instruction in Spring 2020. We use two complementary difference-in differences frameworks, one that leverages within-instructor-by-course variation on whether students started their Spring 2020 courses in person or online and another that incorporates student fixed effects. We estimate the impact of this shift on the academic performance of Virginia’s community college students. With both approaches, we find modest negative impacts (three to six percent) on course completion. Our results suggest that faculty experience teaching a given course online does not mitigate the negative effects. In an exploratory analysis, we find minimal long-term impacts of the switch to online instruction.

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Kelli A. Bird, Benjamin L. Castleman, Brett Fischer, Benjamin T. Skinner.

Recent state policy efforts have focused on increasing attainment among adults with some college but no degree (SCND). Yet little is actually known about the SCND population. Using data from the Virginia Community College System (VCCS), we provide the first detailed profile on the academic, employment, and earnings trajectories of the SCND population, and how these compare to VCCS graduates. We show that the share of SCND students who are academically ready to reenroll and would benefit from doing so may be substantially lower than policy makers anticipate. Specifically, we estimate that few SCND students (approximately three percent) could fairly easily re-enroll in fields of study from which they could reasonably expect a sizable earnings premium from completing their degree.

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