Kelli A. Bird

Colleges have increasingly turned to predictive analytics to target at-risk students for additional support. Most of the predictive analytic applications in higher education are proprietary, with private companies offering little transparency about their underlying models. We address this lack of transparency by systematically comparing two important dimensions: (1) different approaches to sample and variable construction and how these affect model accuracy; and (2) how the selection of predictive modeling approaches, ranging from methods many institutional researchers would be familiar with to more complex machine learning methods, impacts model performance and the stability of predicted scores. The relative ranking of students’ predicted probability of completing college varies substantially across modeling approaches. While we observe substantial gains in performance from models trained on a sample structured to represent the typical enrollment spells of students and with a robust set of predictors, we observe similar performance between the simplest and most complex models.

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Do nudge interventions that have generated positive impacts at a local level maintain efficacy when scaled state or nationwide? What specific mechanisms explain the positive impacts of promising smaller-scale nudges? We investigate, through two randomized controlled trials, the impact of a national and state-level campaign to encourage students to apply for financial aid for college. The campaigns collectively reached over 800,000 students, with multiple treatment arms to investigate different potential mechanisms. We find no impacts on financial aid receipt or college enrollment overall or for any student subgroups. We find no evidence that different approaches to message framing, delivery, or timing, or access to one-on-one advising affected campaign efficacy. We discuss why nudge strategies that work locally may be hard to scale effectively.

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Student loan borrowing for higher education has emerged as a top policy concern. Policy makers at the institutional, state, and federal levels have pursued a variety of strategies to inform students about loan origination processes and how much a student has cumulatively borrowed, and to provide students with greater access to loan counseling. We conducted an experiment to evaluate the impact of an outreach campaign that prompted loan applicants at a large community college to make informed and active borrowing decisions and that offered them access to remote, one-onone assistance from a loan counselor. The intervention led students to reduce their unsubsidized loan borrowing by 7 percent, resulted in worse academic performance, and increased the likelihood of loan default during the three years after the intervention occurred. Our results suggest policy makers and higher education leaders should carefully examine the potential unintended consequences of efforts to reduce student borrowing, particularly in light of growing evidence regarding the counter-intuitive positive relationship between reduced borrowing levels and worse student academic and financial outcomes.

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Despite broad public interest in Veterans' education, there is relatively little evidence documenting the postsecondary trajectories of military service members after they return to civilian life. In the current report we investigate how U.S. Army service member college enrollment and progression trends compare to a similar population of civilians, using Army administrative personnel data merged with administrative records from the National Student Clearinghouse and the Educational Longitudinal Study (ELS) of 2002. Civilians were nearly three times as likely to enroll in college within one year of high school graduation (or one year of separation). Civilians were also much more likely to earn a bachelor’s degree within the period of study than either of the Army samples. While members of minority race/ethnicity groups in both military samples enroll at higher rates than their white counterparts, racial/ethnic minorities do not graduate at higher rates than their white counterparts. We discuss policy implications of our analyses in the final section of our paper.

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