The current study outlines a general process for measuring item-level effort that can be applied to an expanded set of item types and test-taking behaviors (such as omitted or constructed responses). This process, which is illustrated with data from a large-scale assessment program, should improve our ability to detect non-effortful test taking and perform individual score validation.

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.

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.

In this Brookings Institute Chalkboard blog, James Soland shares his work investigating how student test engagement may shape achievement gaps.

In this study, we examine the impact of two techniques to account for test disengagement—(a) removing unengaged test takers from the sample and (b) adjusting test scores to remove rapidly guessed items—on estimates of school contributions to student growth, achievement gaps, and summer learning loss.

Schools are increasingly held accountable for their contributions to students’ academic growth in math and reading. Under The Every Student Succeeds Act, most states are estimating how much schools improve student achievement over time and using those growth metrics to identify the bottom 5% of schools for remediation.

In this study, we apply the Compound Polynomial or “CP” model in a school evaluation context. The CP model addresses the seasonality of student test scores by simultaneously estimating between- and within-year growth.