The Website Accessibility Conformance Evaluation Methodology (WCAG-EM) directs an auditor to select samples of web pages for accessibility evaluation. However, the sampling method of WCAG-EM cannot statistically claim to represent pages outside of the selected sample. To generate an optimal representative sample, we previously proposed OPTIMAL-EM. In this article, we apply the framework to explore the relationships between two metrics in the framework: web page complexity and accessibility. We first cluster pages by their structure by employing t-distributed Stochastic Neighbour Embedding (t-SNE) and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) by representing pages using two approaches: one including all HTML elements and another focusing on block-level HTML elements. We then compare the average complexity of a cluster and the variance of the complexity of a given cluster against the average accessibility of that cluster. To measure their correlations, we conducted a two-stage evaluation: an initial experiment with a website of 388 pages and a validation study with three additional random sites of 500 pages. Our experiments demonstrate that clusters characterised by lower variability in complexity tend to exhibit fewer accessibility barriers, suggesting that standardised and templated web design could be more beneficial to accessibility than more complex pages. Furthermore, the main contribution of this article is showing that by identifying and focusing on clusters with high complexity variance, auditors can target their efforts more effectively, prioritising areas more likely to present accessibility challenges. By providing a more systematic and scalable sampling method, our approach can optimise conformance evaluations of large websites by reducing the human effort involved, focusing on a more targeted assessment of more representative pages.

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.

Eye-tracking data can be used to understand how users interact with web pages. Understanding the eye-movement sequences of multiple users is a challenging task because the sequence followed by each user tends to be different. Scanpath Trend Analysis (STA) brings multiple individual eye-movement sequences together and identifies a representative sequence as a trending path. However, eye-tracking data on a web page is required to determine the trending path. Our aim here is to investigate whether we can train Machine Learning (ML) algorithms to identify trending elements on a web page without collecting eye-tracking data on that web page. This article presents our experiments with different ML classification algorithms towards achieving that goal. To validate the experiments, we used two datasets from previous research, the first one included browsing and searching tasks and the second one included browsing and synthesis tasks. Our experiments show that the k-nearest neighbors algorithm (KNN) model can successfully identify the trending elements in the first dataset for both browsing (F1=≈91%) and searching tasks (F1=≈88%). However, the second dataset’s synthesis task results were not as successful as its browsing task results. Our work here shows that a model can be created to predict the trending elements in web pages solely with web page features but the task is a critical factor in the success of prediction.

Web accessibility evaluation is a costly and complex process due to limited time, resources and ambiguity. To optimise the accessibility evaluation process, we aim to reduce the number of pages auditors must review by employing statistically representative pages, reducing a site of thousands of pages to a manageable review of archetypal pages. Our paper focuses on representativeness, one of six proposed metrics that form our methodology, to address the limitations we have identified with the W3C Website Accessibility Conformance Evaluation Methodology (WCAG-EM). These include the evaluative scope, the non-probabilistic sampling approach, and the potential for bias within the selected sample. Representativeness, in particular, is a metric to assess the quality and coverage of sampling. To measure this, we systematically evaluate five web page representations with a website of 388 pages, including tags, structure, the DOM tree, content, and a mixture of structure and content. Our findings highlight the importance of including structural components in representations. We validate our conclusions using the same methodology for three additional random sites of 500 pages. As an exclusive attribute, we find that features derived from web content are suboptimal and can lead to lower quality and more disparate clustering for optimised accessibility evaluation.

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder, often associated with social and communication challenges and whose prevalence has increased significantly over the past two decades. The variety of different manifestations of ASD makes the condition difficult to diagnose, especially in the case of highly independent adults. A large body of work is dedicated to developing new and improved diagnostic techniques, emphasising approaches that rely on objective markers. One such paradigm is investigating eye-tracking data as a promising and objective method to capture attention-related differences between people with and without autism. This study builds upon prior work in this area that focussed on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work. The proposed approach to data pre-processing is stimulus-independent, suggesting that the improvements in performance shown in these experiments can potentially generalise over other studies that use eye-tracking data for predictive purposes.

Smartphones play a crucial role in daily activities, however, situationally-induced impairments and disabilities (SIIDs) can easily be experienced depending on the context. Previous studies explored the effect of context but mainly done in controlled environments with limited research done in the wild. In this article, we present an in-situ remote user study with 48 participants’ keyboard interaction on smartphones including the performance and context details. We first propose an automated approach for error detection by combining approaches introduced in the literature and with a follow-up study, show that the accuracy of error detection is improved. We then investigate the effect of context on the typing performance based on five dimensions: environment, mobility, social, multitasking, and distraction, and reveal that the context affects participants’ error rate significantly but with individual differences. Our main contribution is providing empirical evidence with an in-situ study showing the effect of context on error rate.

Table mining on the web is an open problem, and none of the previously proposed techniques provides a complete solution. Most research focuses on the structure of the HTML document, but because of the nature and structure of the web, it is still a challenging problem to detect relational tables. Web Content Accessibility Guidelines (WCAG) also cover a wide range of recommendations for making tables accessible, but our previous work shows that these recommendations are also not followed; therefore, tables are still inaccessible to disabled people and automated processing. We propose a new approach to table mining by not looking at the HTML structure, but rather, the rendered pages by the browser. The first task in table mining on the web is to classify relational vs. layout tables, and here, we propose two alternative approaches for that task. We first introduce our dataset, which includes 725 web pages with 9,957 extracted tables. Our first approach extracts features from a page after being rendered by the browser, then applies several machine learning algorithms in classifying the layout vs. relational tables. The best result is with Random Forest with the accuracy of 97.2% (F1-score: 0.955) with 10-fold cross-validation. Our second approach classifies tables using images taken from the same sources using Convolutional Neural Network (CNN), which gives an accuracy of 95% (F1-score: 0.95). Our work here shows that the web’s true essence comes after it goes through a browser and using the rendered pages and tables, the classification is more accurate compared to literature and paves the way in making the tables more accessible.

Abstract Mobile devices and accessing the web have become essential in our daily lives. However, their limitations in terms of both hardware such as the battery, and software capabilities can affect the user experience such as battery drain. There are some best practices for the web page design that are shown to affect the downloading time of web pages. In this study, we report our experience in applying these practices to see their effect on energy saving. We propose two techniques: (1) concatenating external script and stylesheet files and (2) minifying external script and stylesheets that can be used to transcode web pages to improve energy consumption on the client‐side and therefore improve the battery life. We present our experimental architecture, implementation, and a systematic evaluation of these two techniques. The evaluation results show that the proposed techniques can achieve approximately 12% processor energy‐saving and 4% power saving in two different client types, 13% improvement in a typical laptop battery life, and 4% improvement in a typical mobile phone battery life.

Web pages are composed of different kinds of elements (menus, adverts, etc.). Segmenting pages into their elements has long been important in understanding how people experience those pages and in making those experiences “better.” Many approaches have been proposed that relate the resultant elements with the underlying source code; however, they do not consider users’ interactions. Another group of approaches analyses eye movements of users to discover areas that interest or attract them (i.e., areas of interest or AOIs). Although these approaches consider how users interact with web pages, they do not relate AOIs with the underlying source code. We propose a novel approach that integrates web page and eye tracking data driven approaches for automatic AOI detection. This approach segments an entire web page into its AOIs by considering users’ interactions and relates AOIs with the underlying source code. Based on the Adjusted Rand Index measure, our approach provides the most similar segmentation to the ground-truth segmentation compared to its individual components.

People with autism often exhibit different visual behaviours from neurotypical users. To explore how these differences are exhibited on the Web, we model visual behaviour by combining pupillary response, which is an unobtrusive measure of physiological arousal, with eye-tracking scan paths that indicate visual attention. We evaluated our approach with two populations: 19 neurotypical users and 19 users with autism. We observe differences in their visual behaviours as, in certain instances, individuals with autism exhibit a lower arousal response to affective contents. While this is consistent with the literature on autism, we confirm this phenomenon on the Web. We discuss how our modelling method can be used to identify possible UX issues such as the presence of stress, cognitive load and differences in the perception of Web elements in relation to physiological arousal.

Anecdotal evidence suggests that people with autism may have different processing strategies when accessing the web. However, limited empirical evidence is available to support this. This paper presents an eye tracking study with 18 participants with high-functioning autism and 18 neurotypical participants to investigate the similarities and differences between these two groups in terms of how they search for information within web pages. According to our analysis, people with autism are likely to be less successful in completing their searching tasks. They also have a tendency to look at more elements on web pages and make more transitions between the elements in comparison to neurotypical people. In addition, they tend to make shorter but more frequent fixations on elements which are not directly related to a given search task. Therefore, this paper presents the first empirical study to investigate how people with autism differ from neurotypical people when they search for information within web pages based on an in-depth statistical analysis of their gaze patterns.

Web pages typically include many visual elements such as header and footer to support interaction with the user. However, if web pages do not comply with web accessibility guidelines, and these visual elements are not explicitly encoded in the underlying source code, they become inaccessible in alternative presentations, such as audio. This article presents an automatic role detection approach to identify visual elements in web pages and their roles. The system architecture has three major components: automatic identification of visual elements in web pages; automatic generation of heuristic rules from the knowledge base; and application of these rules for automatic annotation of visual elements with their roles. This article first explains the system architecture in detail and then presents both technical and user evaluations of the proposed approach. Our user evaluation shows that the automatic role detection approach has around 70% receptive accuracy, but the proposed knowledge base could be further improved for better results. Our technical evaluation shows that the complexity is an important performance factor in role detection – required resources and execution time increases when the web page has more complex structure.

The number of users required for usability studies has been a controversial issue over 30 years. Some researchers suggest a certain number of users to be included in these studies. However, they do not focus on eye tracking studies for analysing eye movement sequences of users (i.e., scanpaths) on web pages. We investigate the effects of the number of users on scanpath analysis with our algorithm that was designed for identifying the most commonly followed path by multiple users. Our experimental results suggest that it is possible to approximate the same results with a smaller number of users. The results also suggest that more users are required when they serendipitously browse on web pages in comparison with when they search for specific information or items. We observed that we could achieve 75% similarity to the results of 65 users with 27 users for searching tasks and 34 users for browsing tasks. This study guides researchers to determine the ideal number of users for analysing scanpaths on web pages based on their budget and time.

We present an experiment comparing performance of 20 novice evaluators of accessibility carrying out Web Content Accessibility Guidelines 2.0 conformance reviews working individually to performance obtained when they work in teams of two. They were asked to first carry out an individual assessment of a web page. Later on, they were matched randomly to constitute a group of two and they were asked to revise their initial assessment and to produce a group assessment of the same page. Results indicate that significant differences were found for sensitivity (inversely related to false negatives: +8%) and agreement (when measured in terms of the majority view: +10%). Members of groups exhibited strong agreement on the evaluation results among them and with the group outcome. Other measures of validity and reliability are not significantly affected by group work. Practical implications of these findings are that, for example, when it is important to reduce the false-negative rate, then employing a group of two people is more useful than having individuals carrying out the assessment. Openings for future research include further explorations of whether similar results hold for groups larger than two or what is the effect of mixing people with different accessibility background. When novice accessibility evaluators work in groups, their ability to identify all the true problems increases (by 8%). Likewise, reliability of group evaluations increases (by 10%). Individual or group evaluations can be considered as equivalent methods with respect to false positives (if differences up to 8% in correctness are tolerated). Individual or group evaluations can be considered as equivalent methods with respect to overall effectiveness (if differences up to 11% in F -measure are tolerated).

Eye tracking studies have widely been used in improving the design and usability of web pages and in the research of understanding how users navigate them. However, there is limited research in clustering users’ eye movement sequences (i.e., scanpaths) on web pages to identify a general direction they follow. Existing research tends to be reductionist, which means that the resulting path is so short that it is not useful. Moreover, there is little work on correlating users’ scanpaths with visual elements of web pages and the underlying source code, which means the result cannot be used for further processing. In order to address these limitations, we introduce a new concept in clustering scanpaths called Scanpath Trend Analysis (STA) that not only considers the visual elements visited by all users, but also considers the visual elements visited by the majority in any order. We present an algorithm which automatically does this trend analysis to identify a trending scanpath for multiple web users in terms of visual elements of a web page. In contrast to existing research, the STA algorithm first analyzes the most visited visual elements in given scanpaths, clusters the scanpaths by arranging these visual elements based on their overall positions in the individual scanpaths, and then constructs a trending scanpath in terms of these visual elements. This algorithm was experimentally evaluated by an eye tracking study on six web pages for two different kinds of tasks (12 cases in total). Our experimental results show that the STA algorithm generates a trending scanpath that addresses the reductionist problem of existing work by preventing the loss of commonly visited visual elements for all cases. Based on the statistical tests, the STA algorithm also generates a trending scanpath that is significantly more similar to the inputted scanpaths compared to other existing work in 10 out of 12 cases. In the remaining cases, the STA algorithm still performs significantly better than some other existing work. This algorithm contributes to behavior analysis research on the web that can be used for different purposes: for example, re-engineering web pages guided by the trending scanpath to improve users’ experience or guiding designers to improve their design.

The equality of access – accessibility – is difficult to quantify, define, or agree upon. Our previous work analysed the responses of web accessibility specialists in regard to a number of pre-defined definitions of accessibility. While uncovering much, this analysis did not allow us to quantify the communities’ understanding of the relationship accessibility has with other domains and assess how the community scopes accessibility. In this case, we asked over 300 people, with an interest in accessibility, to answer 33 questions surrounding the relationship between accessibility, user experience (UX), and usability; inclusion and exclusion; and evaluation, in an attempt to harmonise our understanding of web accessibility. We found that respondents think that accessibility and usability are highly related and also think that accessibility is applicable to everyone and not just people with disabilities. Respondents strongly agree that accessibility must be grounded on user-centred practices and that accessibility evaluation is more than just inspecting source code; however, they are divided as to whether training in ‘Web Content Accessibility Guidelines’ is necessary or not to assess accessibility. These perceptions are important for usability and UX professionals, developers of automated evaluation tools, and those practitioners running website evaluations.

Previous studies have defined a new type of impairment in which an able-bodied user's behaviour is impaired by both the characteristics of a device and the environment in which it is used. This behavioural change is defined as a situationally-induced impairment and is often associated with small devices used in a mobile setting or constrained environment. Relatively little is known of its extent or the magnitude of the problems faced by the user. Here, we place the work that has been undertaken into a comparative framework of impairment in the context of data input. Our survey finds that 12 problems exist which define all impaired input and we look to understand the similarities and possible solutions in the new impairment group of small device users. For instance, from the literature, we find that users who are both situationally and motor impaired: press keys more than once, do not use enough force and so a key is not activated, and accidentally type keys in reverse order. A wealth of other errors exist in the motor impaired domain but work does not exist to support their existence in the situational domain. However, if commonalities exist for some – it seems work should be enacted to uncover the commonalities of all. Establishing these possible commonalities is important because solutions from one domain can be leveraged into another thereby saving the need to reinvent interventions which already exist elsewhere.

Web accessibility means that disabled people can effectively perceive, understand, navigate, and interact with the web. Web accessibility evaluation methods are needed to validate the accessibility of web pages. However, the role of subjectivity and of expertise in such methods is unknown and has not previously been studied. This article investigates the effect of expertise in web accessibility evaluation methods by conducting a Barrier Walkthrough (BW) study with 19 expert and 57 nonexpert judges. The BW method is an evaluation method that can be used to manually assess the accessibility of web pages for different user groups such as motor impaired, low vision, blind, and mobile users. Our results show that expertise matters, and even though the effect of expertise varies depending on the metric used to measure quality, the level of expertise is an important factor in the quality of accessibility evaluation of web pages. In brief, when pages are evaluated with nonexperts, we observe a drop in validity and reliability. We also observe a negative monotonic relationship between number of judges and reproducibility: more evaluators mean more diverse outputs. After five experts, reproducibility stabilizes, but this is not the case with nonexperts. The ability to detect all the problems increases with the number of judges: With 3 experts all problems can be found, but for such a level 14 nonexperts are needed. Even though our data show that experts rated pages differently, the difference is quite small. Finally, compared to nonexperts, experts spent much less time and the variability among them is smaller, they were significantly more confident, and they rated themselves as being more productive. The article discusses practical implications regarding how BW results should be interpreted, how to recruit evaluators, and what happens when more than one evaluator is hired. Supplemental materials are available for this article. Go to the publisher's online edition of Human-Computer Interaction for statistical details and additional measures for this article. © 2011 Taylor & Francis Group, LLC.

World Wide Web accessibility and best practice audits and evaluations are becoming increasingly complicated, time consuming, and costly because of the increasing number of conformance criteria which need to be tested. In the case of web access by disabled users and mobile users, a number of commonalities have been identified in usage, which have been termed situationally-induced impairments; in effect the barriers experienced by mobile web users have been likened to those of visually disabled and motor impaired users. In this case, we became interested in understanding if it was possible to evaluate the problems of mobile web users in terms of the aggregation of barriers-to-access experienced by disabled users; and in this way attempt to reduce the need for the evaluation of the additional conformance criteria associated with mobile web best practice guidelines. We used the Barrier Walkthrough (BW) method as our analytical framework. Capable of being used to evaluate accessibility in both the disabled and mobile contexts, the BW method would also enable testing and aggregation of barriers across our target user groups. We tested 61 barriers across four user groups each over four pages with 19 experts and 57 non-experts focusing on the validity and reliability of our results. We found that 58% of the barrier types that were correctly found were identified as common between mobile and disabled users. Further, if our aggregated barriers alone were used to test for mobile conformance only four barrier types would be missed. Our results also showed that mobile users and low vision users have the most common barrier types, while low vision and motor impaired users experiencing similar rates of severity in the barriers they experienced. We conclude that the aggregated evaluation results for blind, low vision and motor impaired users can be used to approximate the evaluation results for mobile web users. © 2011 British Informatics Society Limited. Published by Elsevier B.V. All rights reserved.

Web Content Accessibility Guidelines 2.0 (WCAG 2.0) require that success criteria be tested by human inspection. Further, testability of WCAG 2.0 criteria is achieved if 80% of knowledgeable inspectors agree that the criteria has been met or not. In this paper we investigate the very core WCAG 2.0, being their ability to determine web content accessibility conformance. We conducted an empirical study to ascertain the testability of WCAG 2.0 success criteria when experts and non-experts evaluated four relatively complex web pages; and the differences between the two. Further, we discuss the validity of the evaluations generated by these inspectors and look at the differences in validity due to expertise.

Manual accessibility evaluation plays an important role in validating the accessibility of Web pages. This role has become increasingly critical with the advent of the Web Content Accessibility Guidelines (WCAG) 2.0 and their reliance on user evaluation to validate certain conformance measures. However, the role of expertise, in such evaluations, is unknown and has not previously been studied. This paper sets out to investigate the interplay between expert and non-expert evaluation by conducting a Barrier Walkthrough (BW) study with 19 expert and 51 non-expert judges. The BW method provides an evaluation framework that can be used to manually assess the accessibility of Web pages for different user groups including motor impaired, hearing impaired, low vision, cognitive impaired, etc. We conclude that the level of expertise is an important factor in the quality of accessibility evaluation of Web pages. Expert judges spent significantly less time than non-experts; rated themselves as more productive and confident than non-experts; and ranked and rated pages differently against each type of disability. Finally, both effectiveness and reliability of the expert judges are significantly higher than non-expert judges.

This paper presents an eye-tracking study that examines how people use the visual elements of Web pages to complete certain tasks. Whilst these elements are available to play their role in these tasks for sighted users, it is not the case for visually disabled users. This lack of access to some visual elements of a page means that visually disabled users are hindered in accomplishing these tasks. Our previous work has introduced a framework that identifies these elements and then reengineers Web pages such that these elements can play their intended roles in an audio, as well as visual presentation. To further improve our understanding of how these elements are used and to validate our framework, we track the eye movements of sighted users performing a number of different tasks. The resulting gaze data show that there is a strong relationship between the aspects of a page that receive visual attention and the objects identified by our framework. The study also shows some limitations, as well as yielding information to address these short-comings. Perhaps the most important result is the support provided for a particular kind of object called a Way Edge - the visual construct used to group content into sections. There is a significant effect of Way Edges on the distribution of attention across tasks. This is a result that not only provides strong evidence for the utility of re-engineering, but also has consequences for our understanding of how people allocate attention to different parts of a page. We speculate that the phenomenon of 'Banner Blindness' owes as much to Way Edges, as it does to colour and font size.

The importance of the World Wide Web for information dissemination is indisputable. However, the dominance of visual design on the Web leaves visually disabled people at a disadvantage. Although assistive technologies, such as screen readers, usually provide basic access to information, the richness of the Web experience is still often lost. In particular, traversing the Web becomes a complicated task since the richness of visual objects presented to their sighted counterparts are neither appropriate nor accessible to visually disabled users. To address this problem, we have proposed an approach called Dante in which Web pages are annotated with semantic information to make their traversal properties explicit. Dante supports usage of different annotation techniques and as a proof-of-concept in this article, pages are annotated manually which when transcoded become rich. We first introduce Dante and then present a user evaluation which compares how visually disabled users perform certain travel-related tasks on original and transcoded versions of Web pages. We discuss the evaluation methodology in detail and present our findings, which provide useful insights into the transcoding process. Our evaluation shows that, in tests with users, document objects transcoded with Dante have a tendency to be much easier for visually disabled users to interact with when traversing Web pages.

Currently, the vast majority of web sites do not support accessibility for visually impaired users. Usually, these users have to rely on screen readers: applications that sequentially read the content of a web page in audio. Unfortunately, screen readers are not able to detect the meaning of the different page objects, and thus the implicit semantic knowledge conveyed in the presentation of the page is lost. One approach described in literature to tackle this problem, is the Dante approach, which allows semantic annotation of web pages to provide screen readers with extra (semantic) knowledge to better facilitate the audio presentation of a web page. Until now, such annotations were done manually, and failed for dynamic pages. In this paper, we combine the Dante approach with a web design method, WSDM, to fully automate the generation of the semantic annotation for visually impaired users. To do so, the semantic knowledge gathered during the design process is exploited, and the annotations are generated as a by-product of the design process, requiring no extra effort from the designer.

Movement, or mobility, is key to the accessibility, design, and usability of many hypermedia resources (websites); and key to good mobility is context and preview by probing. This is especially the case for visually impaired users when a hypertext anchor is inaccurately described or is described out of context. This means confusion and disorientation. Mobility is similarly reduced when the link target of the anchor has no relationship to the expected information present on the hypertext node (web-page). We suggest that confident movement with purpose, ease, and accuracy can only be achieved when complete contextual information and an accurate description of the proposed destination (preview) are available. Our past work (1) deriving mobility heuristics from mobility models, (2) transforming web-pages based on these heuristics, and(3) building tools to analyse and access these transformed pages; has shown us that a tool to expand context and preview would be useful. In this paper we describe the development of such a middleware tool to automatically and dynamically annotate web-pages with additional context information present within the page, and preview information present within hypertext link destinations found on the page.

Tables remain a persistent problem for visually impaired people using screen readers. Tables are complex structures that are widely used for different purposes such as spatial layout or data summarisation. The multi-dimensional nature of tables challenges the linear interaction styles typically supported by screen readers. To read a table, a user needs to maintain coherency of, and interact with more than one dimension. In this paper, we first characterise why tables are useful in print, but difficult to read in the audio. We present a survey of the relationship between table structure, intention and the reading styles employed to use the content of tables. We then present two different approaches for interacting with tables non-visually. These approaches are designed to support the characteristics of tables that make them such a popular and useful means of conveying information. The first approach provides a small table browser called EVITA (Enabling Visually Impaired Table Access), whose aim is to enable non-visual table browsing and reading in an analogous manner to the print medium. The second approach provides a table lineariser to transform tables into a form such that they can be easily read by screen readers.

Users make journeys through the Web. Web travel encompasses the tasks of orientation and navigation, the environment and the purpose of the journey. The ease of travel, its mobility, varies from page to page and site to site. For visually impaired users, in particular, mobility is reduced; the objects that support travel are inaccessible or missing altogether. Web development tools need to include support to increase mobility. We present a framework for finding and classifying travel objects within Web pages. The evaluation carried out has shown that this framework supports a systematic and consistent method for assessing travel upon the Web. We propose that such a framework can provide the foundation for a semi-automated tool for the support of travel upon the Web.