An intelligent CAT that can deal with disengaged test taking
Wise, S. (2020). An intelligent CAT that can deal with disengaged test taking. In H. Jiao & R. W. Lissitz (Eds), Application of artificial intelligence to assessment (pp. 161-174). Information Age Publishing.
By: Steven Wise
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.
This book was published outside of NWEA. The full text can be found at the link above.
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.
This interactive tool provides context on the typical patterns of achievement and growth in mathematics and reading for private and Catholic schools who take MAP Growth assessments. It provides multiple ways to examine patterns for different groups of students, including by student gender, race/ethnic group, region, and state.
By: Michael Dahlin, Art Katsapis
This user’s guide for the MAP Growth Goal Explorer describes how to use this interactive tool, the benchmarks it uses to provide context on student growth goals, a framework for goal setting, instructions for how to download information from the tool, and answers to frequently asked questions.