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Matt X. Wang

Publications and source records attributed to Matt X. Wang.

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Nonvisual Support for Understanding and Reasoning about Data Structures

Blind and visually impaired (BVI) computer science students face systematic barriers when learning data structures: current accessibility approaches typically translate diagrams into alternative text, focusing on visual appearance rather than preserving the underlying structure essential for conceptual understanding. More accessible alternatives often do not scale in complexity, cost to produce, or both. Motivated by a recent shift to tools for creating visual diagrams from code, we propose a solution that automatically creates accessible representations from structural information about diagrams. Based on a Wizard-of-Oz study, we derive design requirements for an automated system, Arboretum, that compiles text-based diagram specifications into three synchronized nonvisual formats$\unicode{x2013}$tabular, navigable, and tactile. Our evaluation with BVI users highlights the strength of tactile graphics for complex tasks such as binary search; the benefits of offering multiple, complementary nonvisual representations; and limitations of existing digital navigation patterns for structural reasoning. This work reframes access to data structures by preserving their structural properties. The solution is a practical system to advance accessible CS education.

cs.HC

Systems for Scaling Accessibility Efforts in Large Computing Courses

It is critically important to make computing courses accessible for disabled students. This is particularly challenging in large computing courses, which face unique challenges due to the sheer scale of course content and staff. In this experience report, we share our attempts to scale accessibility efforts for a large university-level introductory programming course sequence, with over 3500 enrolled students and 100 teaching assistants (TAs) per year. First, we introduce our approach to auditing and remediating course materials by systematically identifying and resolving accessibility issues. However, remediating content post-hoc is purely reactive and scales poorly. We then discuss two approaches to systems that enable proactive accessibility work. We developed technical systems to manage remediation complexity at scale: redesigning other course content to be web-first and accessible by default, providing alternate accessible views for existing course content, and writing automated tests to receive instant feedback on a subset of accessibility issues. Separately, we established human systems to empower both course staff and students in accessibility best practices: developing and running various TA-targeted accessibility trainings, establishing course-wide accessibility norms, and integrating accessibility topics into core course curriculum. Preliminary qualitative feedback from both staff and students shows increased engagement in accessibility work and accessible technologies. We close by discussing limitations and lessons learned from our work, with advice for others developing similar auditing, remediation, technical, or human systems.

cs.CY