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Web accessibility of university websites in China: An empirical study

Hao Liu · Jing Sun · Zanxiang He · Hongzhan Huang · Xinai Liu · Liming Nie
10.1007/s44443-026-00544-6 405 Views 0 Citations
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Abstract

Abstract
University websites serve as critical digital gateways to higher education and should be accessible to all users. Despite China possessing the world’s largest higher education system, systematic empirical research in this domain remains scarce. To bridge this gap, we utilized three automated tools to quantitatively evaluate the compliance of 993 key pages (e.g., homepages, undergraduate admissions) from 250 Chinese universities with the Web Content Accessibility Guidelines (WCAG 2.2) Level A and AA standards. It is noted that due to tool-specific technical limitations, effective sample sizes varied slightly: aXe (993 pages), Siteimprove (991 pages), and BrowserStack (957 pages). The findings uncover a stark reality that majority of evaluated pages failed to fully meet WCAG 2.2 Level A standards across all three tools used. For instance, aXe identified 36,283 issues (averaging 36.54 per page). A deeper analysis reveals that the majority of these issues were related to the Perceivable principle (69.49%) and contravene foundational Level A criteria (70.58%). Moreover, image-alt, link-name, and color-contrast were identified as the three most prevalent issues. Overall, the analysis indicates that accessibility was inadequately considered during the website development process. As a first step toward improvement, prioritizing the resolution of the three most prevalent issues would address the vast majority of current failures. Furthermore, universities should also invest in accessibility training and research for developers and institutionalize routine reviews of their web pages by both experts and users with disabilities.

Cite this Article (APA)
Hao, L., Jing, S., Zanxiang, H., Hongzhan, H., Xinai, L., Liming, N. (2026). Web accessibility of university websites in China: An empirical study. Journal of King Saud University - Computer and Information Sciences. https://doi.org/10.1007/s44443-026-00544-6
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Published in
ISSN 1319-1578
Quartile Q1
AMS Score 100
Field Computer Science & AI
Publisher Elsevier / King Saud University
Country 🇸🇦 Saudi Arabia
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Publication Details
Year 2026
Language English
Added 06 Jul 2026