Search for: All records

Despite the interest in equity, little research has considered students with disabilities in PreK-12 computer science education. The 2022 Computer Science Teachers Association and Kapor Center facilitated Landscape Survey of PreK-12 CS Teachers, which had over 2200 responses, gives us new insight. There were few significant differences between the experiences and perceptions of teachers with disabilities and those without. Accessibility was the least taught computing concept. Furthermore, teachers reported on a variety of barriers that students with disabilities encounter related to structural barriers, students choosing note to take CS, and teachers' perceptions of student ability. The findings point to the need for interventions related to resources, outreach, and policy. 

The introduction of block-based programming has gradually changed the landscape of programming education, particularly for school children. Block languages today, however, have serious technical barriers to students with disabilities. For example, block languages are generally not screen reader accessible, incompatible with braille, and contain serious problems for users with motor impairments. No student with a disability should ever be denied access to learning computer science and they do not have to be. To help rectify this, we present a new approach to the design of block languages called Quorum Blocks. Quorum Blocks uses a custom hardware accelerated graphical rendering pipeline that takes into account how screen readers and other devices work under the hood. We discuss these technical details and demonstrate that accessibility support can be fully achieved without meaningfully losing either the look of modern blocks or their visual output. We present the results from focus groups that highlight the barriers students faced with a variety of disabilities when using the first version of Quorum Blocks. We focus especially on challenges with low vision users, screen reader users, or those using no mouse and only one hand to type. Block languages built using either our techniques, or on top of our libraries, would become accessible out of the box. 

Sign languages are used as a primary language by approximately 70 million D/deaf people world-wide. However, most communication technologies operate in spoken and written languages, creating inequities in access. To help tackle this problem, we release ASL Citizen, the first crowdsourced Isolated Sign Language Recognition (ISLR) dataset, collected with consent and containing 83,399 videos for 2,731 distinct signs filmed by 52 signers in a variety of environments. We propose that this dataset be used for sign language dictionary retrieval for American Sign Language (ASL), where a user demonstrates a sign to their webcam to retrieve matching signs from a dictionary. Through our generalizable baselines, we show that training supervised machine learning classifiers with our dataset achieves competitive performance on metrics relevant for dictionary retrieval, with 63% accuracy and a recall-at-10 of 91%, evaluated entirely on videos of users who are not present in the training or validation sets. 

Block-based programming environments are very popular for introducing children to programming. Unfortunately, they are not accessible to many children with visual or motor impairments. In this paper we outline why block-based environments should be made accessible for these children, describe current efforts to make environments accessible, and describe how developers can incorporate accessibility into their own block-based programming environments. 

Approximately 10% of computer science and engineering majors have a disability. Students with disabilities face a variety of challenges including those related to stigma around disability, inaccessible tools and instruction, disability disclosure, and a lack of mentors. This BOF will bring together individuals who are interested in increasing the representation of students with disabilities in computing and improving their success. Participants will share strategies to help each other do a better job of including these students in our classes and research projects. Resources related to accessible tools and instruction, universal design of learning, opportunities for students, and more will be shared. 

The College Board's AP Computer Science Principles (CSP) content has become a major new course for introducing K-12 students to the discipline. The course was designed for many reasons, but one major goal was to broaden participation. While significant work has been completed toward equity by many research groups, we know of no systematic analysis of CSP content created by major vendors in relation to accessibility for students with disabilities, especially those who are blind or visually impaired. In this experience report, we discuss two major actions by our team to make CSP more accessible. First, with the help of accessibility experts and teachers, we modified the entire Code.org CSP course to make it accessible. Second, we conducted a one-week professional development workshop in the summer of 2018 for teachers of blind or visually impaired students in order to help them prepare to teach CSP or support those who do. We report here on lessons learned that are useful to teachers who have blind or visually impaired students in their classes, to AP CSP curriculum providers, and to the College Board. 

Blocks-based programming environments are a popular tool to teach children to program, but they rely heavily on visual metaphors and are therefore not fully accessible for children with visual impairments. We evaluated existing blocks-based environments and identified five major accessibility barriers for visually impaired users. We explored techniques to overcome these barriers in an interview with a teacher of the visually impaired and formative studies on a touchscreen blocks-based environment with five children with visual impairments. We distill our findings on usable touchscreen interactions into guidelines for designers of blocks-based environments. 

As conversational agents and digital assistants become increasingly pervasive, understanding their synthetic speech becomes increasingly important. Simultaneously, speech synthesis is becoming more sophisticated and manipulable, providing the opportunity to optimize speech rate to save users time. However, little is known about people’s abilities to understand fast speech. In this work, we provide the first large-scale study on human listening rates. Run on LabintheWild, it used volunteer participants, was screen reader accessible, and measured listening rate by accuracy at answering questions spoken by a screen reader at various rates. Our results show that blind and low-vision people, who often rely on audio cues and access text aurally, generally have higher listening rates than sighted people. The findings also suggest a need to expand the range of rates available on personal devices. These results demonstrate the potential for users to learn to listen to faster rates, expanding the possibilities for human-conversational agent interaction.