Optimising web accessibility evaluation
Population sourcing methods for web accessibility evaluation
Bibliographic Data
| ID | 21642256 |
|---|---|
| Authors | Alexander Hambley (0000-0003-1193-6632, University of Manchester, corresponding author), Yeliz Yeşilada (0000-0001-8501-0205, Middle East Technical University), Markel Vigo (0000-0003-4768-7623, University of Manchester), Simon Harper (0000-0001-9301-5049, University of Manchester) |
| Year | 2025 |
| Volume | 198 |
| Pages | 103472 |
| Publication date | 2025-04-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | International Journal of Human-Computer Studies (JOURNAL) |
| Journal identifiers | ISSN: 1071-5819 • E-ISSN: 1095-9300 |
| Publisher | Elsevier BV (PUBLISHER) |
| DOI | 10.1016/j.ijhcs.2025.103472 |
| OpenAlex | W4407795453 |
| Language | EN |
| References cited | 13 |
Traditional methods for selecting web pages for evaluation lack a systematic approach. Web accessibility is crucial to improve equal access and usability for individuals with disabilities. However, current approaches to accessibility evaluation are often time-consuming and resource-intensive. By optimising the accessibility evaluation process, we can more effectively identify and address accessibility issues on the web. This paper addresses the challenge of efficiently evaluating web accessibility across heterogeneous web pages. Drawing inspiration from census studies, we developed a framework to measure population-sourcing methods that leverage modern templated web development processes. This approach allows us to cluster heterogeneous web pages and select representative samples that reflect the entire cluster, streamlining the evaluation process. In this paper, we demonstrate that our clustering method effectively groups web pages with similar accessibility characteristics. Using statistical tooling and incorporating additional features from server log files, we aim to improve the accuracy and efficiency of accessibility audits. Our findings have several implications for web accessibility evaluation: our approach offers a more systematic and objective way of selecting pages for evaluation, reducing the reliance on ad-hoc heuristics. Secondly, by optimising the evaluation process, we envision that organisations can allocate their resources more efficiently, ensuring a broader coverage of web pages while maintaining high accessibility standards. Finally, incorporating server log files as a data source highlights the potential of leveraging existing web analytics data for accessibility assessment, providing additional insights and opportunities for improvement. • Framework for Web Accessibility Evaluation. OPTIMAL-EM enhances WCAG-EM with six metrics. • Comparison of Population Sourcing. Server logs outperform crawling in coverage. • Impact of Complexity and Popularity. Server logs offer a more balanced page sample. • Practical Implications. OPTIMAL-EM may improve audit efficiency and resource use
Environmental health · Population · Web accessibility · Web Accessibility Initiative · Web application · Web application security · Web development · Web service · Web standards · World Wide Web · Computer Science · Digital Accessibility for Disabilities · Medicine · Mobile Crowdsensing and Crowdsourcing · Web Data Mining and Analysis
| Citation velocity | historical |
|---|---|
| Highly cited | No |