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Program and policy effects
Noncognitive constructs such as self-efficacy, social awareness, and academic engagement are widely acknowledged as critical components of human capital, but systematic data collection on such skills in school systems is complicated by conceptual ambiguities, measurement challenges and resource constraints. This study addresses this issue by comparing the predictive validity of two most widely used metrics on noncogntive outcomes|observable academic behaviors (e.g., absenteeism, suspensions) and student self-reported social and emotional learning (SEL) skills|for the likelihood of high school graduation and postsecondary attainment. Our findings suggest that conditional on student demographics and achievement, academic behaviors are several-fold more predictive than SEL skills for all long-run outcomes, and adding SEL skills to a model with academic behaviors improves the model's predictive power minimally. In addition, academic behaviors are particularly strong predictors for low-achieving students' long-run outcomes. Part-day absenteeism (as a result of class skipping) is the largest driver behind the strong predictive power of academic behaviors. Developing more nuanced behavioral measures in existing administrative data systems might be a fruitful strategy for schools whose intended goal centers on predicting students' educational attainment.
Students with disabilities (SWDs) educated in traditional public schools alongside general education students (GENs) typically move to middle school in sixth grade, rather than continuing in a K-8/12. The documented negative effects of this move on GEN academic outcomes suggests similar negative—and perhaps larger—effects on SWDs. Using an instrumental variables strategy and NYC data on nine cohorts of students, we find the middle school transition causes a 0.30 (0.16) standard deviation decline in SWD math (ELA) performance and increases grade retention. Low-income SWDs and SWDs with a specific learning disability or emotional disturbance fare worse. However, the move does not widen the SWD-GEN gap, suggesting the need to ease the middle school transition for all students.
States increasingly require prospective teachers to pass exams for program completion and initial licensure, including the recent controversial roll-out of the educative Teacher Performance Assessment (edTPA). We leverage the quasi-experimental setting of different adoption timing by states and analyze multiple data sources containing a national sample of prospective teachers and students of new teachers in the US. With extensive controls of concurrent policies, we find that the edTPA reduced prospective teachers in traditional route programs, less-selective and minority-concentrated universities. Contrary to the policy intention, we do not find evidence that edTPA increased student test scores.
Few question the value of teacher-student relationships (TSRs) for educational outcomes. TSRs are positively associated with students’ achievement and engagement, as well as teachers’ well-being. Building and maintaining these crucial classroom relationships, however, is not easy. Drawing on prominent motivation theories in educational psychology, I present the Motivating Teacher-Student Relationships framework for understanding what motivates teachers to build positive TSRs. In particular, I focus on how teachers’ motivational beliefs about TSRs energize, direct, and sustain their efforts to engage in relationship-building behaviors and, thus, lead to positive relationships with their students. To build positive TSRs, teachers must believe it is their role to build TSRs, value TSRs, and believe they can successfully build TSRs (i.e., have relational self-efficacy). These beliefs are shaped by teachers’ sociocultural contexts and can facilitate or undermine the development of these learning relationships. With a greater understanding of how motivational beliefs influence social relationships, the field of education can more effectively develop theoretically grounded interventions to improve TSRs and mitigate inequality.
This study synthesizes existing research on the implementation of tutoring programs which we define as one-to-one or small-group instruction in which a human tutor supports students grades K-12 in an academic subject area. Tutoring has emerged as an especially promising strategy for supporting students’ academic success with strong causal evidence finding large, positive effects on students' math and reading test scores across grade levels. Prior studies have reviewed this causal evidence of effects, but none have summarized the evidence on implementation. We iteratively developed search and selection criteria to identify studies addressing key research questions and synthesized these 40 studies which employ a range of research methodologies to describe how tutoring is implemented and experienced. We find that existing research provides rich descriptions of tutoring implementation within specific programs of focus, with most studies describing after-school tutoring and small-scale programs run by university professors. While few elements of implementation are studied in depth across multiple studies, common patterns emerge. Tutoring program launch is often facilitated by strategic relationships between schools and external tutoring providers and strengthened by transparent assessments of program quality and effectiveness. Successful tutoring implementation often hinges on the support of key school leaders with the power to direct the use of school funding, space, and time. Tutoring setting and schedule, tutor recruitment and training, and curriculum identification influence whether students are able to access tutoring services and the quality of the instruction provided. Ultimately, the evidence points to strong tutoring being driven by positive student-tutor relationships through which tutors provide instruction strategically targeted for students’ strengths and needs driving towards a long-term academic goal.
Policymakers have renewed calls for expanding instructional time in the wake of the COVID-19 pandemic. We establish a set of empirical facts about time in school, synthesize the literature on the causal effects of instructional time, and conduct a case study of time use in an urban district. On average, instructional time in U.S. public schools is comparable to most high-income countries, with longer days but shorter years. However, instructional time varies widely across U.S. public schools with a 90th-10th percentile difference of 190 total hours. Empirical literature confirms that additional time can increase student achievement, but how this time is structured matters. Our case study suggests schools might also recover substantial lost learning time within the existing school day.
Nearly all states with public prekindergarten programs use mixed-delivery systems, with classrooms in both public schools and community-based settings. However, experts have long raised concerns about systematic inequities by setting within these public systems. We used data from five large-scale such systems that have taken steps to improve equity by setting (Boston, New York City, Seattle, New Jersey, and West Virginia) to conduct the most comprehensive descriptive study of prekindergarten setting differences to date. Our public school sample included 2,395 children in 383 classrooms in 152 schools, while our community-based sample is comprised of 1,541 children in 201 classrooms in 103 community-based organizations (CBOs). We examined how child and teacher demographic characteristics, structural and process quality features, and child gains differed by setting within each of these systems. We found evidence of sorting of children and teachers by setting within each locality, including of children with higher baseline skills and more educated teachers into public schools. Where there were differences in quality and children’s gains, these tended to favor public schools. The localities with fewer policy differences by setting – NJ and Seattle – showed fewer differences in quality and child gains. Our findings suggest that inequities by setting are common, appear consequential, and deserve more research and policy attention.
There is increasing concern about risky behaviors and poor mental health among school-aged youth. A critical factor in youth well-being is school attendance. This study evaluates how school organization and structure affect health outcomes by examining the impacts of a popular urban high school reform -- “small schools” -- on youth risky behaviors and mental health, using data from New York City. To estimate a causal estimate of attending small versus large high schools, we use a two-sample-instrumental-variable approach with the distance between student residence and school as the instrument for school enrollment. We consider two types of small schools – “old small schools,” which opened prior to a system-wide 2003 reform aimed at increasing educational achievement and “new small schools,” which opened in the wake of that reform. We find that girls enrolled in older small schools are less likely to become pregnant, and boys are less likely to be diagnosed with mental health disorders than their counterparts in large schools. Both girls and boys enrolled in more recently opened small schools, however, are more likely to be diagnosed with violence-associated injuries and (for girls only) with mental health disorders. These disparate results suggest that improving a school’s organization and inputs together is likely more effective in addressing youth risky behaviors than simply reducing school size.
Preparing K-12 students for careers in science, technology, engineering and mathematics (STEM) fields is an ongoing challenge confronting state policymakers. We examine the implementation of a science graduation testing requirement for high-school students in Massachusetts, beginning with the graduating class of 2010. We find that the design of the new requirement was quite complicated, reflecting the state’s previous experiences with test-based accountability, a broad consensus on policy goals among key stakeholders, and the desire to afford flexibility to local schools and districts. The consequences for both students and schools, while largely consistent with the goals of increasing students’ skills and interest in STEM fields, were in many cases unexpected. We find large differences by demographic subgroup in the probabilities of passing the first science exam and of succeeding on retest, even when conditioning on previous test-score performance. Our results also show impacts of science exit-exam performance for students scoring near the passing threshold, particularly on the high-school graduation rates of females and on college outcomes for higher-income students. These findings demonstrate the importance of equity considerations in designing and evaluating ambitious new policy initiatives.
Measures of student disadvantage—or risk—are critical components of equity-focused education policies. However, the risk measures used in contemporary policies have significant limitations, and despite continued advances in data infrastructure and analytic capacity, there has been little innovation in these measures for decades. We develop a new measure of student risk for use in education policies, which we call Predicted Academic Performance (PAP). PAP is a flexible, data-rich indicator that identifies students at risk of poor academic outcomes. It blends concepts from emerging “early warning” systems with principles of incentive design to balance the competing priorities of accurate risk measurement and suitability for policy use. PAP is more effective than common alternatives at identifying students who are at risk of poor academic outcomes and can be used to target resources toward these students—and students who belong to several other associated risk categories—more efficiently.