The Stanford Education Data Archive (SEDA) launched in 2016 to provide nationally comparable, publicly available test score data for U.S. public school districts. I introduce a special collection of six articles that each use SEDA to lend their questions and findings a national scope. Together, these articles demonstrate a range of uses of SEDA…

Many research agencies are now requiring that data collected as part of funded projects be shared. However, the practice of data sharing in education sciences has lagged these funder requirements. We assert that this is likely because researchers generally have not been made aware of these requirements and of the benefits of data sharing.…

This AERA Open special topic concerns the large emerging research area of education data science (EDS). In a narrow sense, EDS applies statistics and computational techniques to educational phenomena and questions. In a broader sense, it is an umbrella for a fleet of new computational techniques being used to identify new forms of data, measures,…

In addition to providing a set of techniques to analyze educational data, I claim that data science as a field can provide broader insights to education research. In particular, I show how the bias-variance tradeoff from machine learning can be formally generalized to be applicable to several prominent educational debates, including debates around…

Research has shown that mathematical proficiency gaps are related to students' and schools' indicators of poverty, with fewer studies on neighborhood effects on achievement gaps. Although this literature has accounted for students' nesting within schools, so far, methodological constraints have not allowed researchers to formally account for…

The prospect of "big data" at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing…

The Stanford Educational Data Archive (SEDA) is the first data set to allow comparisons of district academic achievement and growth from Grades 3 to 8 across the United States, shining a light on the distribution of educational opportunities. This study describes a convergent validity analysis of the SEDA growth estimates in mathematics and…

Aggregate survey responses collected from students are commonly used by universities to compare effective educational practices across program majors, and to make high-stakes decisions about the effectiveness of programs. Yet if there is too much heterogeneity among student responses within programs, the program-level averages may not…

This article describes issues in measuring school contexts with an eye toward understanding students' experiences and outcomes. I begin with an overview of the conceptual underpinnings related to measuring contexts, briefly describe the initiatives at the National Center for Education Statistics to measure school contexts, and identify possible…

Forty years ago, the National Center for Education Statistics initiated the national longitudinal studies program in response to congressional concern for policy-relevant information on school-to-work transitions. This program has grown substantially, and not unexpectedly, questions have arisen about its usefulness and present operation. This…

School violence and bullying are two public health concerns with consequences for youth in and out of school, for families, students, and community members. In this article, a social-ecological framework is briefly described as a way to understand bullying and school violence; then the National Center for Educational Statistics (NCES) longitudinal…