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While teacher preparation in the United States continues a long period of decline, the largest-producing state, Texas, is experiencing substantial changes in how it prepares teachers. The number of teachers prepared by traditional university pathways continues to decline, and the number from alternative pathways is rising. Using extensive data from Texas, we find that traditionally prepared teachers from universities obtain significantly higher student learning gains than alternatives. We use value-added models to estimate changes in student test scores in many grade levels and test subjects as a function of teacher preparation pathway. We compare all Traditional programs to all Alternative programs, and we compare all For-Profit programs to all Not for-Profit programs. For most subjects and grade levels, students learn significantly more from Traditional or Not for-Profit program teachers: 0.02 to 0.05 in standard deviation units. There is not one significant estimate in any model where students learn more from Alternative and For-Profit programs teachers than they do from Traditional and Not For-Profit program teachers.

Local education agencies (LEAs) continue to use value-added models (VAMs) for  teacher evaluation policies and purposes, often with consequences attached. Although the Every  Student Succeeds Act (ESSA) provides more flexibility to LEAs, few have discontinued VAM use, suggesting they interpret VAMs as a valid measure of teacher effectiveness. In this systematic review, we used a framework built on the Standards of Educational and Psychological  Testing (AERA et al., 2014) to examine validity evidence contained in 75 articles published in  high-quality, peer-reviewed journals in which article authors supported or challenged user  interpretations and uses of VAMs. Results with implications for educational policy are presented.

We aim to estimate school value-added dynamically in time. Our principal motivation for doing so is to establish school effectiveness persistence while taking into account the temporal dependence that typically exists in school performance from one year to the next. We propose two methods of incorporating temporal dependence in value-added models. In the first we model the random school effects that are commonly present in value-added models with an auto-regressive process. In the second approach, we incorporate dependence in value-added estimators by modeling the performance of one cohort based on the previous cohort’s performance. An identification analysis allows us to make explicit the meaning of the corresponding value-added indicators: based on these meanings, we show that each model is useful for monitoring specific aspects of school persistence. Furthermore, we carefully detail how value-added can be estimated over time. We show through simulations that ignoring temporal dependence when it exists results in diminished efficiency in value-added estimation while incorporating it results in improved estimation (even when temporal dependence is weak). Finally, we illustrate the methodology by considering two cohorts from Chile’s national standardized test in mathematics.

This article analyzes the constitutive and productive effects of one US middle school's teacher evaluation system, and the way it operates to (re)make teacher subjects. Using transcripts from interviews with teachers and evaluators, as well as policy and system protocol documents, the article demonstrates how a positive evaluation discourse has become the structuring framework for legitimizing high-stakes evaluation, normalizing constant surveillance and audit, and producing new teacher subjects who assemble themselves as perpetually imperfect. The article illustrates how the evaluation system has produced a culture of compliance where the teacher's professional and ethical identity is constituted by the tools, practices, and norms of evaluation. It argues that the evaluation system is an onto-epistemic regime that fundamentally reconstitutes the purpose, identity, and function of the teacher subject. This article raises new questions about who a teacher can be, what types of attitudes can be imagined, and what actions can be exercised.

There is no final consensus regarding which covariates should be used (in addition to prior achievement) when estimating value-added (VA) scores to evaluate a school’s effectiveness. Therefore, we examined the sensitivity of evaluations of schools’ effectiveness in math and language achievement to covariate selection in the applied VA model. Four covariate sets were systematically combined, including prior achievement from the same or different domain, sociodemographic and sociocultural background characteristics, and domain-specific achievement motivation. School VA scores were estimated using longitudinal data from the Luxembourg School Monitoring Programme with some 3600 students attending 153 primary schools in Grades 1 and 3. VA scores varied considerably, despite high correlations between VA scores based on the different sets of covariates (.66 < r < 1.00). The explained variance and consistency of school VA scores substantially improved when including prior math and prior language achievement in VA models for math and prior language achievement with sociodemographic and sociocultural background characteristics in VA models for language. These findings suggest that prior achievement in the same subject, the most commonly used covariate to date, may be insufficient to control for between-school differences in student intake when estimating school VA scores. We thus recommend using VA models with caution and applying VA scores for informative purposes rather than as a mean to base accountability decisions upon.

Are equal educational opportunities sufficient to narrow long-standing economic and racial inequalities in achievement? In this article, I test the hypothesis that poor and minority students benefit less from effective elementary school teachers than do their nonpoor and white peers, thus exacerbating inequalities. I use administrative data from public elementary schools in North Carolina to calculate value-added measures of teachers' success in promoting learning, and I assess benefits for different students. Results suggest that differential benefits of effective teachers uniquely exacerbate black–white inequalities but do not contribute to economic achievement gaps. Racial differences are small, on average, relative to the benefits for all groups; are not explained by differences in prior achievement; and are largest for low-achieving students. Teacher-related learning opportunities are crucial for all students, but these results point to a disconnect between typical school learning opportunities and low-achieving minority students.

Until recently, legal challenges to using value-added models (VAMs) throughout the United States (US) for high-stakes teacher evaluative decisions (e.g., merit pay, tenure, and termination) were unsuccessful, especially in the state of Florida. Hence, prior and still, multiple teachers throughout Florida have been terminated or involuntarily transferred out of their teaching positions. One Florida teacher’s case is of particular interest, in that this teacher continues to fight to be re-positioned into his/her post, arguing that the VAM estimates used against him/her were reliable, albeit invalid and biased. The purpose of this case study, accordingly, was to explore this unique and complicated case, to help the Court, and others, discern and determine whether this termination decision was empirically and legally justified. Research-policy-practice implications, via the Court, are also discussed.

Education agencies are evaluating teachers using student achievement data. However, very little is known about the comparability of test-based or \"value-added\" metrics across districts and the extent to which they capture variability in classroom practices. Drawing on data from four urban districts, we found that teachers were categorized differently when compared within versus across districts. In addition, analyses of scores from two observation instruments, as well as qualitative viewing of lesson videos, identified stark differences in instructional practices across districts among teachers who received similar within-district value-added rankings. These patterns were not explained by observable background characteristics of teachers, suggesting that factors beyond labor market sorting likely played a key role.

Measures of school-level growth in student outcomes are common tools for assessing the impacts of schools. The vast majority of these measures use standardized tests as the outcome of interest, even though emerging evidence demonstrates the importance of social-emotional learning (SEL). In this article, we present results from using the first large-scale panel surveys of students on SEL to produce school-level value-added measures by grade for growth mindset, self-efficacy, self-management, and social awareness. We found substantive differences across schools in SEL growth, with magnitudes of differences similar to those for growth in academic achievement. In contrast, we found that the goodness of fit of the value-added model was considerably lower when the outcome variables were measures of SEL constructs rather than of academic achievement. In addition, the across-school variance in the average level of the SEL measures was proportionally much smaller than that for academic measures. These findings recommend caution in interpreting measures as the causal impacts of schools on SEL, though they also do not rule out important school effects.

In the 2011–12 school year, the Newark Public School district (NPS) launched a set of educational reforms supported by a gift from Facebook CEO Mark Zuckerberg and Priscilla Chan. Using data from 2008–09 through 2015–16, the authors evaluate the change in Newark students’ achievement growth relative to similar students and schools elsewhere in New Jersey. They measure achievement growth using a “value-added” model, controlling for prior achievement, demographics, and peer characteristics. By the fifth year of reform, Newark saw statistically significant gains in English language arts (ELA) achievement growth and no significant change in math achievement growth. Perhaps because of the disruptive nature of the reforms, growth declined initially before rebounding in later years. Much of the improvement was attributed to shifting enrollment from lower- to higher-growth district and charter schools