This session from the National Council on Measurement in Education 2020 virtual conference presents new research findings on understanding and managing test-taking disengagement (presentation begins at 22:55).

This report presents the results of a mode comparability study conducted based on empirical data to evaluate how scores from MAP Growth administered on the constraint-based engine (CBE) compare to those administered on the current MAP Growth engine known as COLO.

This study investigated effort‐moderated (E‐M) scoring, in which item responses classified as rapid guesses are identified and excluded from scoring, and its affect on score distortion from disengaged test taking.

The 2020 MAP Growth Norms report presents mathematics, reading, language arts and science achievement and growth patterns for students attending public schools across the U.S. It includes details on the methodological approach, information on MAP Growth assessments, the tested student population and post-stratification weighting, growth modeling, and implications of the study for research and practice, as well as tables showing student and school status and growth norms, status percentiles, growth distributions, and growth percentiles

This study explored how response style affects estimates of growth.

This book presents varied applications of artificial intelligence (AI) in test development, including research and successful examples of using AI technology in automated item generation, automated test assembly, automated scoring, and computerized adaptive testing.

Rapid-guessing can distort test scores and adversely affect measurement. New research shows how disengaged responses can also distort content representation.