Innovations in reporting & assessment
Emerging technologies allow for a variety of methods to assess students and report data specific to the needs of different stakeholders. These various approaches can result in assessments that are more engaging for students, along with reporting that provides more insightful, useful information for students, families, and educators.
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.
By: Greg King
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.
Products: MAP Growth
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.
Products: MAP Growth
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.
By: Steven Wise, Megan Kuhfeld, John Cronin
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.
By: Meg Guerreiro, Elizabeth Barker, Janice Johnson
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.
By: Steven Wise, Megan Kuhfeld