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Educator preparation, professional development, performance and evaluation
We examine heterogenous responses to job-embedded performance incentives along two dimensions theorized to drive motivation: (i) expectations of success when faced with easier versus more difficult tasks, and (ii) ones’ social identity as part of a marginalized group (in our case, race). Compared to the largely lab-based literature on this topic, we leverage data from a fully implemented teacher evaluation system from the District of Columbia Public Schools over a seven-year period (2009-10 through 2015-16). Our regression discontinuity estimates reveal not only that task difficulty and social/racial identity drive much of the incentive effects, but also that there is a strong interaction between the two. Low-performing teachers threatened with dismissal improved much more on tasks with low difficulty and high expectations of success, relative to more difficult tasks (roughly 0.3 SD versus 0.15 SD). These trends were particularly pronounced for Black teachers, who experienced fewer successes than White teachers in the evaluation system generally. We also find that high-performing Black teachers were less responsive than White teachers to an incentive to increase their base pay. At the same time, the responses of Black teachers to the salary incentive were malleable and tracked closely with district-led redesign efforts that aimed to ensure greater equity in terms of teachers most likely to reap the benefits of this incentive.
Teachers are the most important school-specific factor in student learning. Yet, little evidence exists linking teacher professional learning programs and the various strategies or components that comprise them to student achievement. In this paper, we examine a teacher fellowship model for professional learning 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 teachers and school leaders to provide 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 model increased student proficiency rates in math and English language arts on state achievement exams. Further, student achievement benefitted from a more sustained duration of teacher participation in the fellowship model, and the impact on student achievement varied depending on the share of a school’s teachers who participated in the fellowship model and the extent to which teachers independently selected into the fellowship model or were appointed to participate by school leaders. Taken together, findings from this paper should inform professional learning organizations, schools, and policymakers on the design, implementation and impact of teacher professional learning.
The pursuit of multiple educational outcomes makes teaching a complex craft subject to potential conflicts and competing commitments. Using a dataset in which teachers were randomly assigned to students paired with videotapes of instruction, we both document and unpack such a tradeoff. Upper-elementary teachers who excel at raising students’ math test scores often are less successful at improving student-reported engagement in class (and vice versa). Further, the teaching practices that improve math test scores (e.g., cognitively demanding content) can simultaneously decrease engagement. At the same time, paired quantitative and qualitative analyses reveal two areas of practice that support both outcomes: active mathematics with opportunities for hands-on participation; and established routines and procedures to proactively organize the classroom environment. In addition to guiding practice-based teacher education, our mixed-methods analysis can serve as a model for rigorously studying and identifying dimensions of “good” teaching that promote multidimensional student development.
We examine the dynamic nature of student-teacher match quality by studying the effect of having a teacher for more than one year. Using data from Tennessee and panel methods, we find that having a repeat teacher improves achievement and decreases absences, truancy, and suspensions. These results are robust to a range of tests for student and teacher sorting. High-achieving students benefit most academically and boys of color benefit most behaviorally. Effects increase with the share of repeat students in a class suggesting that classroom assignment policies intended to promote sustained student-teacher relationships such as looping may have even larger benefits.
This study introduces the signal weighted teacher value-added model (SW VAM), a value-added model that weights student-level observations based on each student’s capacity to signal their assigned teacher’s quality. Specifically, the model leverages the repeated appearance of a given student to estimate student reliability and sensitivity parameters, whereas traditional VAMs represent a special case where all students exhibit identical parameters. Simulation study results indicate that SW VAMs outperform traditional VAMs at recovering true teacher quality when the assumption of student parameter invariance is met but have mixed performance under alternative assumptions of the true data generating process depending on data availability and the choice of priors. Evidence using an empirical data set suggests that SW VAM and traditional VAM results may disagree meaningfully in practice. These findings suggest that SW VAMs have promising potential to recover true teacher value-added in practical applications and, as a version of value-added models that attends to student differences, can be used to test the validity of traditional VAM assumptions in empirical contexts.
Growing literature documents the promise of active learning instruction in engaging students in college classrooms. Accordingly, faculty professional development (PD) programs on active learning have become increasingly popular in postsecondary institutions; yet, quantitative evidence on the effectiveness of these programs is limited. Using administrative data and an individual fixed effects approach, we estimate the effect of an active learning PD program on student performance and persistence at a large public institution. Findings indicate that the training improved subsequent persistence in the same field. Using a subset of instructors whose instruction was observed by independent observers, we identify a positive association between training and implementation of active learning teaching practices. These findings provide suggestive evidence that active learning PD has the potential to improve student outcomes.
The unprecedented challenges of teaching during COVID-19 prompted fears of a mass exodus from the profession. We examine the extent to which these fears were realized using administrative records of Massachusetts teachers between 2015-16 and 2021-22. Relative to pre-pandemic levels, average turnover rates were similar going into the fall of 2020 but increased by 17 percent going into the fall of 2021. The fall 2021 increases were particularly high among newly hired teachers (31 percent increase), but were lower among Black and Hispanic/Latinx teachers (5 percent increases among both groups). Ethnoracial diversity of new hires increased during the pandemic, in part due to reduced professional licensure requirements. Together, these changes led to small increases in the overall ethnoracial diversity of Massachusetts teachers, but improvements to early-career retention will be needed to ensure long-term stability and diversity within the workforce.
Instructional coaching is an attractive alternative to one-size-fits-all teacher training and development in part because it is purposefully differentiated: programming is aligned to individual teachers’ needs and implemented by an individual coach. But, how much of the benefit of coaching as an instructional improvement model depends on the specific coach with whom a teacher works? Collaborating with a national teacher training and development organization, TNTP, we find substantial variability in effectiveness across coaches in terms of changes in teachers’ classroom practice (0.43 standard deviations). The magnitude of coach effectiveness heterogeneity is close to average coaching program effects identified in other research. These findings suggest that identifying, recruiting, and supporting highly skilled coaches will be key to scaling instructional coaching programs.
While a growing body of literature has documented the negative impacts of exclusionary punishments, such as suspensions, on academic outcomes, less is known about how teachers vary in disciplinary behaviors and the attendant impacts on students. We use administrative data from North Carolina elementary schools to examine the extent to which teachers vary in their use of referrals and investigate the impact of more punitive teachers on student attendance and achievement. We also estimate the effect of teachers' racial bias in the use of referrals on student outcomes. We find more punitive teachers increase student absenteeism and reduce student achievement. Moreover, more punitive teachers negatively affect the achievement of students who do not receive disciplinary sanctions from the teacher. Similarly, while teachers with a racial bias in the use of referrals do not negatively affect academic outcomes for White students, they significantly increase absenteeism and reduce achievement for Black students. The results suggest punitive disciplinary measures do not aid teachers in productively managing classrooms; rather, teachers taking more punitive stances may undermine student engagement and learning. Moreover, bias in teachers' referral usage contributes to inequities in student outcomes.
A growing literature uses value-added (VA) models to quantify principals' contributions to improving student outcomes. Principal VA is typically estimated using a connected networks model that includes both principal and school fixed effects (FE) to isolate principal effectiveness from fixed school factors that principals cannot control. While conceptually appealing, high-dimensional FE regression models require sufficient variation to produce accurate VA estimates. Using simulation methods applied to administrative data from Tennessee and New York City, we show that limited mobility of principals among schools yields connected networks that are extremely sparse, where VA estimates are either highly localized or statistically unreliable. Employing a random effects shrinkage estimator, however, can alleviate estimation error to increase the reliability of principal VA.