Scalable Programs and Policies for Promoting Student Success

Abstract

Goldin and Katz (2008) identified investment in human capital as a critical component of the United States’ economic flourishing in the twentieth century. Large-scale investments in secondary and postsecondary education expanded the economy and raised standards of living throughout the income distribution. Toward the end of the twentieth century, though, the accumulation of human capital failed to keep pace with technological progress. Demand for skilled labor began to outpace supply in the early 1980s, which slowed economic growth and widened economic inequality (Goldin & Katz, 2008; Levy & Murnane, 2012). As of 2015, at the secondary level, more than 20% of students fail to graduate on-time (U.S. Department of Education, 2015), and rates have been unresponsive to increases in the economic returns to high school graduation relative to dropping out (Murnane 2013). At the postsecondary level, Carnevale and colleagues (2013) estimated that, in 2020, 65% of U.S. jobs will require postsecondary education, but less than half of the 2004 high school graduation cohort earned a college degree by 2012 (Lauff & Ingels, 2013). The last decade has seen many policy responses for improving rates of high school graduation and postsecondary attainment, particularly for students considered to be disadvantaged in terms of their access to educational opportunities or at greater risk of failing to attain at a level commensurate to their capacity. I contribute to this body of research through three quantitative studies of scalable policies and programs. In the first study, I estimate the effect of Massachusetts’s Early Warning Indicator System (EWIS). Beginning in the 2012-2013 school year, the Massachusetts Department of Elementary and Secondary Education (ESE) began using a statistical model to estimate the probability of each student failing to meet a key educational milestone. The information on each student’s risk label and risk factors was available to districts and schools before the beginning of the school year. In response to the data, personnel could target interventions or reconsider policies to better support at-risk students. In the second study, I use item response theory to study how the treatment of advanced courses in the college admission process may affect access to selective colleges for students from lower socioeconomic backgrounds. The high school grade point average (GPA) is often adjusted to account for nominal indicators of course rigor, such as “honors” or “Advanced Placement.” Adjusted GPAs—also known as weighted GPAs—are frequently used for computing students’ rank in class and in the college admission process. I propose a two-step approach for estimating the correct weighting parameters. Correcting biased weights used in policy is potentially a free and fair way to improve college access and the matching of students to colleges. In the third study, I estimate the causal effect of an expansion of AP course offerings on students’ postsecondary outcomes. Using longitudinal data from Kentucky, where a program systematically increased AP offerings beginning in 2008, I exploit the timing of the rollout to compare outcomes for cohorts who had differential opportunities and incentives for AP course participation. Collectively, the three studies contribute to a growing body of research on scalable approaches to improving educational attainment for disadvantaged students.