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Educator preparation, professional development, performance and evaluation
When employees expect evaluation and performance incentives will continue (or begin) in the future, the potential future rewards create an incentive to invest in relevant skills today. Because skills benefit job performance, the effects of evaluation can persist after the rewards end or even anticipate the start of rewards. I provide empirical evidence of these dynamics from a quasi-experiment in Tennessee schools. New performance measures improve teachers’ value-added contributions to student achievement. But improvements are twice as large when the teacher also expects future rewards linked to future scores. Value-added remains at the now higher level after performance incentives end.
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.
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.
Why do employers offer pensions? We empirically explore two theoretical rationales, namely that pensions may improve worker effort and worker selection. We examine these hypotheses using administrative measures on effort and output in public schools around the pension-eligibility notch. Worker effort and output do not fall as workers cross the eligibility threshold, implying that pensions may not elicit additional effort. As for selection, we find that pensions retain low-value-added and high-value-added workers at the same rate, suggesting pensions have little or no influence on selection.
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.
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.
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.
Despite the common title of “coach,” definitions of high-quality coaching vary tremendously across models and programs. Yet, few studies make comparisons across different models to understand what is most helpful, for whom, and under what circumstances. As a result, practitioners are left with many options and little evidence-based direction. This is exacerbated by the literature’s focus on more abstract features of coaching practice (e.g. building trust), leaving practitioners to figure out what concrete discourse strategies support these goals. This paper begins to address these challenges by introducing a taxonomy of coaching “moves,” parsing the concrete details of coach discourse. While the taxonomy is informed by the literature, it highlights conceptual possibilities rather than providing a list of empirically-grounded or “evidence-based” strategies. In doing so, this taxonomy may serve as a common language to guide future work exploring how coach discourse shapes teacher development, synthesizing across studies, and supporting coach practice.
Much recent debate among policymakers and policy advocates focuses on whether states should reduce teacher licensure requirements to ease the burdens of recruiting high quality teachers to the workforce. We examine the effectiveness of individuals who entered the teacher workforce in Massachusetts during the pandemic by obtaining an emergency license, which requires only a bachelor’s degree. Our results show that, in 2021-22, newly hired emergency licensed teachers: 1) were largely rated as proficient (82%) in their performance evaluation ratings and 2) had similar measures of student test score growth as their traditionally licensed peers. However, we find suggestive evidence that emergency licensed teachers with no prior employment in Massachusetts public schools and no prior engagement with the teacher pipeline (i.e., enrollment in teacher preparation, attempting licensure exams) received lower performance ratings and had lower measures of student test score growth in English Language Arts. Taken together, these results encourage the creation of additional flexibility in licensure requirements for those who have demonstrated prior efforts to join the educator pipeline.
We extend teacher evaluation research by estimating a reformed evaluation system's plausibly causal average effects on rural student achievement, identifying the settings where evaluation works, and incorporating evaluation expenditures. That the literature omits these contributions is concerning as research implies it hinders evidence-based teacher evaluation policymaking for rural districts, which outnumber urban districts. We apply a difference-in-differences framework to Missouri administrative data. Missouri districts could design and maintain reformed systems or outsource these tasks for a small fee to organizations like the Network for Educator Effectiveness (NEE), an evaluation system created for rural users. NEE does not affect student achievement on average but it improves math, and possibly reading, achievement in rural schools where the average student's prior-year achievement score is below the state average or the average teacher's years of experience are below the state average.