This visualization was developed to provide state-level insights into how students performed on MAP Growth in the 2020–2021 school year. Assessments are one indicator, among many, of the student impact from COVID-19. Our goal with this tool is to create visible data that informs academic recovery efforts that will be necessary in the 2022 school year and beyond.

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

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.

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 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 report presents the results of a mode comparability study conducted through simulations to evaluate how scores from MAP Growth administered on the constraint-based engine (CBE) compare to those administered on the current MAP Growth engine known as COLO.