The more frequent collection of response time data is leading to an increased need for an understanding of how such data can be included in measurement models. Models for response time have been advanced, but relatively limited large-scale empirical investigations have been conducted. We take advantage of a large data set from the adaptive NWEA MAP Growth Reading Assessment to shed light on emergent features of response time behavior.

This paper describes a method for identifying partial engagement and provides validation evidence to support its use and interpretation. When test events indicate the presence of partial engagement, effort-moderated scores should be interpreted cautiously.

This guide explains the analyses, statistics, terms, and data included in the NWEA state dashboard, Exploring the Educational Impacts of COVID-19.

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 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 paper reports on whether transfer learning can improve the prediction of the difficulty and response time parameters for ≈ 18,000 multiple-choice questions from a high-stakes medical exam.