.png){kind=icon}
返回
Optimising web accessibility evaluation: Population sourcing methods for web accessibility evaluation
.png){kind=icon}
OA
AI
DOI：10.1016/j.ijhcs.2025.103472.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 amore 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.
Keyword:
Web accessibility
Automated evaluation
Evaluation methods
Web crawling
Unsupervised learning
Clustering
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
I
IF:
5.1
论文数:
2.9K
被引数:
8.9K
机构
引用论文
Microtubule plus-ends within a mitotic cell are ‘moving platforms’ with anchoring, signalling and force-coupling roles.png)
Open BiologyOAAI
Open BiologyIF0
OAAI分享收藏分享收藏
查看更多内容