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.
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.
The MAP Growth Goal Explorer is designed to support and simplify the goal-setting process by showing a range of possible fall-to-spring growth goals against the backdrop of important academic benchmarks.
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
Do response styles affect estimates of growth on social-emotional constructs? Evidence from four years of longitudinal survey scores
This study explored how response style affects estimates of growth.