A Probabilistic Model and Metrics for Estimating Perceived Accessibility of Desktop Applications in Keystroke-Based Non-Visual Interactions

Abstract

Perceived accessibility of an application is a subjective measure of how well an individual with a particular disability, skills, and goals experiences the application via assistive technology. This paper first presents a study with 11 blind users to report how they perceive the accessibility of desktop applications while interacting via assistive technology such as screen readers and a keyboard. The study identifies the low navigational complexity of the user interface (UI) elements as the primary contributor to higher perceived accessibility of different applications. Informed by this study, we develop a probabilistic model that accounts for the number of user actions needed to navigate between any two arbitrary UI elements within an application. This model contributes to the area of computational interaction for non-visual interaction. Next, we derive three metrics from this model: complexity, coverage, and reachability, which reveal important statistical characteristics of an application indicative of its perceived accessibility. The proposed metrics are appropriate for comparing similar applications and can be fine-tuned for individual users to cater to their skills and goals. Finally, we present five use cases, demonstrating how blind users, application developers, and accessibility practitioners can benefit from our model and metrics.