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Preparing, recruiting, and retaining high-quality teachers into the profession has been a concern of policy makers and practitioners for some time. Teacher attrition is problematic and costly for schools and districts. However, relatively few studies have investigated the relationship between preservice teacher quality and teacher attrition. In this study, we analyze data from an apprenticeship-style teacher preparation program to understand the relationship between a measure of preservice teacher quality—student teachers’ observational scores—and their decisions to (a) enter into the profession, and (b) stay in the profession within the first 2 years after graduation. We find that more qualified student teachers are more likely to enter into the profession and stay in the profession, even after controlling for student teachers’ demographic characteristics and their academic achievement.

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Background: Immigrants and their children are the fastest-growing demographic group in the United States, and schools are often the first social institution young immigrants engage with on a sustained basis. As such, the academic achievement of immigrant students can be viewed as an indicator of their incorporation and a predictor of educational and employment outcomes in adulthood. In this study, we examined the factors associated with differences in mathematics achievement between first, second, and third-plus generation students in the US. Methods: We analyzed the data from the Programme for International Student Assessment (PISA) 2012. Our analytic sample included 3700 15 year-old students attending US public and private schools. We used information on students' background and school characteristics from the student and school questionnaires. We used multiple linear regression models to predict mathematics achievement. To address the sampling design of PISA and the use of plausible values we fitted the models using the IDB Analyzer. Results: Our analysis shows that the families and schools of second-generation students are more similar to their first-generation than their third-plus generation peers. Once we control for student background characteristics and school contextual factors, the achievement gap between first-generation students and their second and third-plus generation peers disappears. Our results suggest that what we observed as generational differences in achievement are more likely to be gender, racial, and socioeconomic gaps. Conclusions: Our findings imply that student background and school contextual factors counteract some of the disadvantages that first-generation students face in the US. Our results also support existing evidence about the second-generation advantage in academic achievement. Taken together, these findings suggest that mathematics achievement can be addressed by policies and practices that support all students alongside policies and practices that target immigrant students.

Value-added models (VAMs) are being used in education to link the contribution of individual teachers and schools to students' learning. The use of VAMs has been surrounded by controversy and high-profile public debates. On April 8, 2014 the American Statistical Association (ASA) released a statement on VAMs related to their use in education practice. In this article, we lay out the discussion of the main points raised in the ASA statement within the large amount of scholarly literature published over the past decade in statistical, education, and economics journals. We identify the issues that are critical for the understanding of the VAMs' strengths and weaknesses, and related consequences of their use for high-stakes decision-making. We conclude that the cautionary points raised in the ASA statement are supported by the findings in the existing research that, with a few exceptions, challenges the assumptions underlying the use of VAMs and demonstrates the issues that should be taken into consideration when using VAMs for consequential decisions.

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