The Accessibility of Data Visualizations on the Web for Screen Reader Users: Practices and Experiences During COVID-19

Data visualization has become an increasingly important means of effective data communication and has played a vital role in broadcasting the progression of COVID-19. Accessible data representations, however, have lagged behind, leaving areas of information out of reach for many blind and visually impaired (BVI) users. In this work, we sought to understand (1) the accessibility of current implementations of visualizations on the web;(2) BVI users' preferences and current experiences when accessing data-driven media;(3) how accessible data representations on the web address these users' access needs and help them navigate, interpret, and gain insights from the data;and (4) the practical challenges that limit BVI users' access and use of data representations. To answer these questions, we conducted a mixed-methods study consisting of an accessibility audit of 87 data visualizations on the web to identify accessibility issues, an online survey of 127 screen reader users to understand lived experiences and preferences, and a remote contextual inquiry with 12 of the survey respondents to observe how they navigate, interpret, and gain insights from accessible data representations. Our observations during this critical period of time provide an understanding of the widespread accessibility issues encountered across online data visualizations, the impact that data accessibility inequities have on the BVI community, the ways screen reader users sought access to data-driven information and made use of online visualizations to form insights, and the pressing need to make larger strides towards improving data literacy, building confidence, and enriching methods of access. Based on our findings, we provide recommendations for researchers and practitioners to broaden data accessibility on the web. © 2023 Copyright held by the owner/author(s). Publication rights licensed to ACM.

LEARNING LAB METHODOLOGY FOR EXPANSIVE LEARNING AND INCLUSIVE EDUCATION DURING THE COVID-19 PANDEMIC IN BRAZIL

The global policies of inclusive education often ignored local knowledge and cultural-historical ecologies. As a result, the top-down policies become either irrelevant or oppressive. This study presents a formative intervention study, Learning Lab, conducted in Brazil to design a new system at specialized school for blind and visually impaired students. Fourteen practitioners engaged in nine meetings with the final goal of producing a new system of inclusive education for students with multiple disabilities during the COVID-19 Pandemic. We will present the expansive learning actions, which educators took as a conduit for critical dialogue, collective agency and expansive learning for designing the future of their school. © ISLS.

Deploying an Instance Segmentation Algorithm to Implement Social Distancing for Prosthetic Vision

The COVID-19 pandemic outbreak is causing a dramatic worsening in the already complicated living conditions of blind and visually impaired individuals. Social distancing is the most effective strategy to limit virus spread, but is extremely difficult for blind people to actuate. Here we propose a deep-learning algorithm to recognize and locate in space people and other categories of objects from RGB-D images. The algorithm, based on Mask R-CNN, performs semantic segmentation on RGB images and uses depth maps to extract information about the relative distance of the instances. It was evaluated using Salient Person dataset and RGB-D Scenes Dataset v.2, and proved effective in segmenting and locating instances. This preliminary work could be a valuable starting point for developing a technology to assist the visually impaired in implementing social distancing. © 2022 IEEE.