This document provides an overview of the research underlying MAP Reading Fluency with Coach’s AI-powered intelligent reading tutor and the research on key elements of early literacy instruction. It describes the components of the MAP Reading Fluency with Coach pedagogy and the research base supporting each component.

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 executive summary outlines results from the Content Proximity spring 2022 pilot study, including information on the validity, reliability, and test score comparability of MAP Growth assessments that leverage this new item-selection algorithm.

The purpose of this research report is to provide detailed information about updates to the MAP Growth item-selection algorithm. This brief includes results from the Content Proximity pilot study, including information on the validity, reliability, and test score comparability of MAP Growth assessments that leverage this new item-selection algorithm.

This study compared the test taking disengagement of students taking a remotely administered an adaptive interim assessment in spring 2020 with their disengagement on the assessment administered in-school during fall 2019.

This study evaluates the effects of asking items throughout the passage (i.e., embedding items) to achieve a more precise measure of reading comprehension by removing barriers for students to demonstrate their understanding. Results showed a significant impact of embedding comprehension items within reading passages on the measurement of student achievement in comparison to answering items at the end of the passage.

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.