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MAP Growth math assessment data corpus

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Evidence · no claims cited

Description

A large corpus of NWEA MAP Growth mathematics assessment data, including standard and accessible test administrations, used to study item accessibility and quality for students with and without visual impairments.

Components

  • MAP Growth math assessment data corpus for accessibility research: The study draws on MAP Growth math assessment data from two populations: about 29.5 million students taking standard versions of the assessment and 48,845 students taking accessible versions. This large-scale corpus enabled the researchers to "identified high-quality items, those that measured achievement for both students with and without VI equally well, and low-quality items, which showed differences between the two groups of students." The corpus is the empirical foundation for the item-quality analysis and the machine-learning prediction work described in the brief. (Kang Xue (2022))

Claims

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Key Sources

  • Kang Xue, Elizabeth Barker. (2022). Using Artificial Intelligence (AI) to improve math accessibility for students with visual impairments. https://www.nwea.org/resources/using-artificial-intelligence-ai-to-improve-math-accessibility-for-students-with-visual-impairments/
Page metadata
Field Value
type product
id map-growth-math-assessment-data-corpus
title MAP Growth math assessment data corpus
description A large corpus of NWEA MAP Growth mathematics assessment data, including standard and accessible test administrations, used to study item accessibility and quality for students with and without visual impairments.
status review
moved_from element/map-growth-math-accessibility-data-corpus
product_kind dataset
generated by process:sweep-kinds on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — kang-xue-2022

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