SEDA provides a unique measure of educational opportunity across the United States. New research supports SEDA achievement scores, but also reveals some differences in growth estimates.

How does rapid-guessing differ from solution behavior? Research provides insight into test disengagement and how disengagement should be managed in scoring.

Learn more about our examination of student disengagement and how it may bias estimates of effectiveness based on observed test results.

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.

This paper briefly discusses the trade-offs involved in making such a transition, and then focuses on a relatively unexplored benefit of computer-based tests – the control of construct-irrelevant factors that can threaten test score validity.

When computer-based tests are used, disengagement can be detected through occurrences of rapid-guessing behavior. This empirical study investigated the impact of a new effort monitoring feature that can detect rapid guessing, as it occurs, and notify proctors that a test taker has become disengaged.

In this study, we condition results from a variety of detection methods used to identify disengaged survey responses on response times. We then show how this conditional approach may be useful in identifying where to set response time thresholds for survey items, as well as in avoiding misclassification when using other detection methods.