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 research study is the first time of applying the thinking of semi-supervised learning into CDM. Also, we used the validating test to choose the appropriate parameters for the ANNs instead of using typical statistical criteria, such as AIC, BIC.

Approaches to test score use and test purpose lack the well-developed methodological guidelines and established sources of evidence available for intended score interpretation. We argue in this paper that this lack fails to reflect the ultimate purpose of a test score—to help solve an important problem faced by intended test users.

To avoid the subjectivity of having a single person evaluate a construct of interest (e.g., a student’s self-efficacy in school), multiple raters are often used. This study provides a model for estimating growth in the presence of multiple raters.

This study uses reading test scores from over 728,923 3rd–8th-grade students in 2,056 schools across the US to compare threshold-setting methods to detect noneffortful item responses. and so helps provide guidance on the tradeoffs involved in using a given method to identify noneffortful responses.

This study identifies students’ academic trajectories in the middle grades relative to a set of college readiness benchmarks. We apply math and reading college readiness benchmarks to rich longitudinal data for more than 360,000 students across the nation. Student-level and school-level demographic characteristics significantly predict academic trajectories.