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.

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.

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.

This study presents a framework that uses academic trajectories in the middle grades for identifying students in need of intervention and providing targeted support.