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As states incorporate measures of college readiness into their accountability systems, school and district leaders need effective strategies to identify and support students at risk of not enrolling in college. Although there is an abundant literature on early warning indicators for high school dropout, fewer studies focus on indicators for college enrollment, especially those that are simple to calculate and easy for practitioners to use. This study explores three potential indicators of college readiness that educational leaders may consider using as part of an early warning system for college enrollment. Using district administrative data, our analysis shows that an indicator based on attendance, grades, and advanced course-taking is slightly more effective at predicting college enrollment than indicators based on course failures or standardized test scores. However, the performance of these indicators varies across different student demographic and socioeconomic subgroups, highlighting the limitations of these measures and pointing to areas where they may need to be supplemented with contextual information. Through event history analysis, we demonstrate that the ninth grade is a particularly challenging year for students, especially those who are male, Black, Hispanic, or economically disadvantaged. These results suggest that educational leaders ought to consider identifying and targeting students at risk of not attending college with additional resources and support during the freshman year of high school.
Each year, over a thousand public schools in the US close due to declining enrollments and chronic low performance, displacing hundreds of thousands of students. Using Texas administrative data and empirical strategies that use within-student across-time and within-school across-cohort variation, I explore the impact of school closures on students' educational and labor market outcomes. The findings indicate that experiencing school closures results in disruptions in both test scores and behavior. While the drop in test scores is recovered within three years, behavioral issues persist. This study further finds decreases in post-secondary education attainment, employment, and earnings at ages 25–27. These impacts are particularly pronounced among students in secondary education, Hispanic students, and those from originally low-performing schools and economically disadvantaged families.
Teachers’ professional identities are the foundation of their practice. Previous scholarship has largely overlooked the extent to which the broader reform culture shapes teachers’ professional identities. In this study, I draw on survey data from 950 teachers across four US states (California, New York, Florida, and Texas) to examine the extent to which teachers’ professional identities are associated with what I term “institutionalized conceptions” of their roles. Across diverse state policy contexts, I find that teachers draw upon a shared set of institutionalized conceptions of their roles, which are associated with their professional identities. The findings suggest that the taken-for-granted ways society frames teaching may be associated with dimensions of teachers’ professional identity, such as self-efficacy and professional commitment.
Home-visitation programs have improved child development in low- and middle-income countries, but they are costly to scale due to their reliance on trained workers. We evaluated an inexpensive and low-tech alternative with 2,433 caregivers of children aged 6 to 30 months served by 250 public childcare centers in Uttarakhand, India: automated phone calls offering parenting advice. The intervention was implemented largely as intended, with more than two-thirds of caregivers completing at least 10 calls. Yet, counter to expectations, it had negative but statistically insignificant effects on caregivers’ knowledge and interactions with their children, reduced their self-efficacy (by 0.11 standard deviations), and increased their anxiety (by 0.10 standard deviations). Consistent with this pattern, it had precisely estimated null effects on children’s development and language. An analysis of program materials suggests four reasons why the program may not have had the desired effects.
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.
Do residential neighbors affect each others' schooling choices? We exploit oversubscription lotteries in Chile's centralized school admission system to identify the effect of close neighbors on application and enrollment decisions. A student is 5-7% more likely to rank a high school as their first preference and to attend that school if their closest neighbor attended it the prior year. These effects are stronger among boys and applicants with lower parents' education and prior academic achievement, measured by previous scores in national standardized tests. Lower-achieving applicants are more likely to follow neighbors when their closest neighbor's test scores are higher. A neighbor enrolling in a school with one s.d. higher school effectiveness, peer composition, or school climate induces increases of 0.02-0.04 s.d. in the applicant's attended school. Our findings suggest that targeted policies aimed at increasing information to disadvantaged families have the potential to alleviate these frictions and generate significant multiplier effects.
Teacher shortages are a persistent challenge in the United States. I evaluate the effectiveness of an innovative pilot program that allowed principals to hand-select experienced staff members and paraeducators already working in schools to lead classrooms. Pilot educators are predominantly Black or African American. Districts reported randomly assigning students to teachers, and my analysis cannot reject randomization. Controlling for demographics and baseline scores, I find that students assigned to these pilot teachers perform just as well as those assigned to traditionally licensed teachers on average and outperform their peers in math. My results point to an untapped resource of potential teachers and underscore the value of principals’ local knowledge to identify capable candidates for teaching positions.
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.
Teachers are the most important school-specific factor in student learning. Yet, little evidence exists linking teacher professional development programs and the strategies or activities that comprise them to student achievement. In this paper, we examine a fellowship model for professional development designed and implemented by Leading Educators, a national nonprofit organization that aims to bridge research and practice to improve instructional quality and accelerate learning across school systems. During the 2015-16 and 2016-17 school years, Leading Educators conducted its fellowship program for two cohorts of instructional leaders, such as department chairs, mentor teachers, instructional coaches, and assistant principals, to provide these educators ongoing, collaborative, job-embedded professional development and to improve student achievement. Relying on quasi-experimental methods, we find that a school’s participation in the fellowship program significantly increased student proficiency rates in English language arts and math on state achievement exams. The positive impact was concentrated in the first cohort and in just one of three regions, and approximately 80 percent of treated schools were charters. Student achievement benefitted from a more sustained duration of participation in the fellowship program, varied depending on the share of a school’s educators who participated in the fellowship, and differed based on whether fellows independently selected into the program or were appointed to participate by their school leaders. Taken together, findings from this paper should inform professional learning organizations, schools, and policymakers on the design, implementation, and impact of educator professional development.
In-person tutoring has been shown to improve academic achievement. Though less well-researched, virtual tutoring has also shown a positive effect on achievement but has only been studied in grade five or above. We present findings from the first randomized controlled trial of virtual tutoring for young children (grades K-2). Students were assigned to 1:1 tutoring, 2:1 tutoring, or a control group. Assignment to any virtual tutoring increased early literacy skills by 0.05-0.08 SD with the largest effects for 1:1 tutoring (0.07-0.12 SD). Students initially scoring well below benchmark and first graders experienced the largest gains from 1:1 tutoring (0.15 and 0.20 SD, respectively). Effects are smaller than typically seen from in-person early literacy tutoring programs but still positive and statistically significant, suggesting promise particularly in communities with in-person staffing challenges.