Math assessment items can be classified as high-quality or low-quality for students with visual impairments based on whether they measure achievement equally well for students with and without VI¶
Claim · All claims
Evidence · 1 study · associationalr?·q2
Currency · newest study 2022 · checked against its sources 2026-10-09 (automated, 1 of 1 entry) · not reviewed by a person
Subclaims¶
Reading the evidence codes
q is the design tier, i the impact, n the sample, and the kind with its r
the rigour judged within that kind: what each code means.
This claim: no standardized effect is recorded in observations/ for it yet, so no benchmark reading is possible.
q2 i? Using MAP Growth math assessment data, researchers identified high-quality items that measured achievement for students with and without VI equally well, and low-quality items that showed differences between the two groups. → Kang Xue 2022
Evidence¶
Kang Xue 2022¶
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/
q2 · i? · associational · r?
Observational analysis of MAP Growth math assessment data from about 29.5 million students on standard versions and 48,845 students on accessible versions. The study "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."
"Using data from about 29.5 million students taking standard versions of the MAP Growth math assessment, and 48,845 students taking accessible versions, we 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."
Discussion¶
Related Claims¶
- The item-quality and prediction work is part of a larger project aiming to make online math more accessible to students with visual impairments — a broader claim this one bears on
- Three word embedding methods and three classifiers were introduced to predict item quality for accessible math assessments — related
Page metadata
| Field | Value |
|---|---|
type |
claim |
id |
map-growth-item-quality-vi-classification |
title |
Math assessment items can be classified as high-quality or low-quality for students with visual impairments based on whether they measure achievement equally well for students with and without VI |
description |
Math assessment items can be classified as high-quality or low-quality for students with visual impairments based on whether they measure achievement equally well for students with and without VI |
status |
review |
evidence_strength |
moderate |
newest_evidence |
2022 |
source_checked |
2026-10-09 |
generated |
by process:wiki-ingest on 2026-10-08 |
trust |
unverified — no one has confirmed this page's content |
sources |
1 — kang-xue-2022 |
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