What happens when test takers disengage? Understanding and addressing rapid guessing
How does rapid-guessing differ from solution behavior? Research provides insight into test disengagement and how disengagement should be managed in scoring.
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This Bellwether Education Partners webinar examines how education research can continue to offer meaningful, relevant information to policymakers and practitioners after the “lost year” of data from COVID disruptions to schools and testing.
By: Matthew Soldner, Megan Kuhfeld, Dan Goldhaber, Constance Lindsay, Allison Crean Davis
Parameter estimation accuracy of the effort-moderated IRT model under multiple assumption violations
This session from the National Council on Measurement in Education 2020 virtual conference presents new research findings on understanding and managing test-taking disengagement.
By: James Soland, Joseph Rios
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).
Do students rapidly guess repeatedly over time? A longitudinal analysis of student test disengagement, background, and attitudes
This session from the 2020 National Council on Measurement in Education virtual conference presents new research findings on understanding and managing test-taking disengagement. (Presentation begins at 22:55).
Topics: School & test engagement
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 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.
By: Steven Wise