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K-12 Education
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.
Longitudinal models of individual growth typically emphasize between-person predictors of change but ignore how growth may vary within persons because each person contributes only one point at each time to the model. In contrast, modeling growth with multi-item assessments allows evaluation of how relative item performance may shift over time. While traditionally viewed as a nuisance under the label of “item parameter drift” (IPD) in the Item Response Theory literature, we argue that IPD may be of substantive interest if it reflects how learning manifests on different items or subscales at different rates. In this study, we present a novel application of the Explanatory Item Response Model (EIRM) to assess IPD in a causal inference context. Simulation results show that when IPD is not accounted for, both parameter estimates and their standard errors can be affected. We illustrate with an empirical application to the persistence of transfer effects from a content literacy intervention on vocabulary knowledge, revealing how researchers can leverage IPD to achieve a more fine-grained understanding of how vocabulary learning develops over time.
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.
Many decentralized matching markets experience high rates of instability due to information frictions. This paper analyzes these frictions in a particularly unstable U.S. market, the labor market for first-year school teachers. We develop and estimate a dynamic, partial equilibrium model of labor mobility that incorporates non-pecuniary information frictions for school climate and teacher workload. In terms of reducing turnover, a policy that improves information outperforms each alternative considered, including targeted wage premiums at hard-to-staff schools, large retention bonuses, and relaxed tenure requirements. Replicating the gains made through information revelation requires retention bonuses valued at 35% of teachers’ current salaries.
College students make job decisions without complete information. As a result, they may rely on misleading heuristics (“interesting jobs pay badly”) and pursue options misaligned with their goals. We test whether highlighting job characteristics changes decision making. We find increasing the salience of a job’s monetary benefits increases the likelihood college students apply by 196%. In contrast, emphasizing prosocial, career, or social benefits has no effect, despite students identifying these benefits as primary motivators for applying. The study highlights the detrimental incongruencies in students’ decision making alongside a simple strategy for recruiting college students to jobs that offer enriching experiences.
The National Assessment of Educational Progress (NAEP) has tested the civic, or citizenship knowledge of students across the nation at irregular intervals since its very inception. Despite advancements in reading and mathematics, evidenced by results from the National Assessment of Educational Progress (NAEP), civics proficiency has remained consistently low, which raises concerns among educators and policymakers. This study attempts to provide those educators and policymakers with state-level predictions, not currently provided for the civics assessment. This research addresses this gap in state-level civics education data by applying multilevel regression with poststratification (MRP) to NAEP's nationally representative civics scores, yielding state-specific estimates that account for student demographics. A historical analysis of NAEP's development underscores its significance in national education and highlights the challenges of transitioning to state-level reporting, particularly for civics, which lacks state-level generalizability. Furthermore, this paper evaluates NAEP's frameworks, questioning their alignment with civics education's evolving needs, and investigates the presence of opportunity gaps in civics knowledge across gender and racial/ethnic lines. By comparing MRP estimates with published NAEP results, the study validates the method's credibility and emphasizes the potential of MRP in educational research. The findings reveal persistent racial/ethnic disparities in civic knowledge, with profound implications for civics instruction and policy. The research concludes by stressing the necessity for state-specific data to inform education policy and practice, advocating for teaching methods that enhance civic understanding and engagement, and suggesting future research directions to address the uncovered disparities.
Mounting evidence supporting the advantages of a diverse teacher workforce prompts policymakers to scrutinize existing recruitment pathways. Following four cohorts of Maryland public high-school students over 12 years reveals several insights. Early barriers require timely interventions, aiding students of color in achieving educational milestones that are prerequisites for teacher candidacy (high school graduation, college enrollment). While alternative pathways that bypass traditional undergraduate teacher preparation may help, current approaches still show persistent racial disparities. Data simulations underscore the need for race-conscious policies specifically targeting or differentially benefiting students of color, as race-neutral strategies have minimal impact. Ultimately, multiple race-conscious policy solutions addressing various educational milestones must demonstrate significant effects—approximately 30% increases—to reshape the teacher workforce to align with student body demographics.
Does state implementation of Education Savings Accounts (ESAs), which are voucher-like taxpayer-funded subsidies for children to attend private schools, increase tuition prices? We analyze a novel longitudinal dataset for all private schools in Iowa and Nebraska, neighboring states that adopted ESAs in the same legislative session, with Iowa’s implementation beginning first. By leveraging state and grade-level variation in eligibility, we provide new causal evidence that ESAs led Iowa private schools to increase tuition. Increases varied by the percentage of the grade eligible for ESAs. When eligibility was universal (kindergarten), private schools increased prices 21-25%, compared with 10-16% in grades with partial eligibility. In contrast, private schools did not increase tuition in pre-K, which was ineligible for ESAs. If a goal of ESAs is to extend private school access to new families, the substantial tuition increases they produce may limit access.
We examine the state of the U.S. K-12 teaching profession over the last half century by compiling nationally representative time-series data on four interrelated constructs: occupational prestige, interest among students, the number of individuals preparing for entry, and on-the-job satisfaction. We find a consistent and dynamic pattern across every measure: a rapid decline in the 1970s, a swift rise in the 1980s extending into the mid 1990s, relative stability, and then a sustained decline beginning around 2010. The current state of the teaching profession is at or near its lowest levels in 50 years. We identify and explore a range of hypotheses that might explain these historical patterns including economic and sociopolitical factors, education policies, and school environments.
Assessing instruction quality is a fundamental component of any improvement efforts in the education system. However, traditional manual assessments are expensive, subjective, and heavily dependent on observers’ expertise and idiosyncratic factors, preventing teachers from getting timely and frequent feedback. Different from prior research that focuses on low-inference instructional practices, this paper presents the first study that leverages Natural Language Processing (NLP) techniques to assess multiple high-inference instructional practices in two distinct educational settings: in-person K-12 classrooms and simulated performance tasks for pre-service teachers. This is also the first study that applies NLP to measure a teaching practice that has been demonstrated to be particularly effective for students with special needs. We confront two challenges inherent in NLP-based instructional analysis, including noisy and long input data and highly skewed distributions of human ratings. Our results suggest that pretrained Language Models (PLMs) demonstrate performances comparable to the agreement level of human raters for variables that are more discrete and require lower inference, but their efficacy diminishes with more complex teaching practices. Interestingly, using only teachers’ utterances as input yields strong results for student-centered variables, alleviating common concerns over the difficulty of collecting and transcribing high-quality student speech data in in-person teaching settings. Our findings highlight both the potential and the limitations of current NLP techniques in the education domain, opening avenues for further exploration.