Learning styles (LS) have been used for classifying students by their preferences relative to taking information in, processing it and demonstrating their ability in the context of education. This paper investigates the role of LS in K-12 education by considering the manner in which student LS are assessed and the extent to which they have informed K-12 instruction.

This study examined the relationships between poverty and a school’s academic performance (both student achievement and growth). Educators, advocates, and policymakers can use these data to shape how people look at the performance of schools in their communities and to inform education policy (e.g., the effect of evaluating schools based on achievement vs. growth).

This study examined developmental trends in academic achievement gaps between poverty and race/ethnicity groups from school entry to middle school using two large longitudinal data sets. We used time-varying effect modeling (TVEM) to estimate how the associations among race/ethnicity, poverty status, and math and reading achievement vary across continuous age from age 5 to age 15.

In this article, we examined the prevalence of rapid guessing to determine if this behavior varied by grade, subject, and teacher, and evaluated if rapid guessing influenced teacher value-added estimates. We observed differences in rapid guessing across grades, subjects, and teachers; however, this behavior did not appear to have a substantive effect on teacher value-added estimates.

This study examines whether test effort differs by student subgroup, including by race and gender. The sensitivity of achievement gap estimates to any differences in test effort is also considered.

Achievement gaps are a metric of fundamental importance to U.S. practice and policy. Gap estimates are often used to measure the effectiveness and fairness of the education system at a given point in time, over the course of decades, and as children progress through school.

The development of academic English proficiency and the time it takes to reclassify to fluent English proficient status are key issues in English learner (EL) policy. This article develops a shared random effects model (SREM) to estimate English proficiency development and time to reclassification simultaneously, treating student-specific random effects as latent covariates in the time to reclassification model.