This report documents the procedure used to produce the achievement and growth user norms for a series of the course-specific MAP® Growth™ subject tests, including Algebra 1, Geometry, Algebra 2, Integrated Math I, Integrated Math II, Integrated Math III, and Biology/Life Science. Among these tests, Integrated Math I, Integrated Math II, Integrated Math III, and Biology/Life Science were the first time to have their norms available. The remaining tests, i.e., Algebra 1, Geometry, and Algebra 2, had their norms updated including receiving more between-term growth norms by using more recent test events. Procedure for norm sample selection and a model-based approach using the multivariate true score model (Thum & He, 2019) that factors out known imprecision of scores to generate the norms are also provided in detail, along with the snapshots of the achievement and growth norms for each test.

New research using longitudinal data provides evidence that deaf and hard of hearing (DHH) students continue to build skills in math and reading throughout grades 2 to 8, challenging assumptions that DHH students’ skills plataeu in elementary grades.

This report documents the norming study procedure used to produce the achievement and growth user norms for English MAP Reading Fluency Foundational Skills.

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 MAP Growth theory of action describes key features of MAP Growth and its position in a comprehensive assessment system.

This study compares estimation of symptom networks with Bayesian GLASSO- and Horseshoe priors to estimation using the frequentist GLASSO using extensive simulations.