2024 ASEE Annual Conference & Exposition

Tactile Learning: Making a Computer Vision Course Accessible through Touched-Based Interfaces

Presented at Transformative Learning in STEM: Accessibility, Social Impact, and Inclusivity in Higher Education

The term "visual learner" is a ubiquitous concept in education. It is often associated with experiential- or example-based teaching that helps the students understand a concept through its application. However, for those with visual impairments, visual learning may not be an option. The Royal National Institute for the Blind reports that there are significant barriers to learning technical content for blind individuals, including access to visual resources and, correspondingly, difficulty interpreting visual concepts. In this work, we discuss experiences and key takeaways from adapting aspects of a computer science graduate course to be more accessible for the blind. The course teaches advanced machine learning methods rooted in computer vision and gesture-based methods in order to build sketch recognition systems. Additionally, it emphasizes elements of human-computer interaction and interface design, many of which are visual concepts. In order to adapt the curriculum, we used a high-resolution tactile display capable of mirroring imagery from a video display into a depth map that could be felt. This enabled the dual presentation of visual content as tactile surface maps. Through this process, we learned several best practices in terms of how to create content that transfers well from one modality to another, and we also developed a number of guidelines for creation of teaching materials like notes and assignments in a way that is more screen-reader friendly. This paper shares key takeaways while also communicating student and teacher perspectives on developing, teaching, and using these materials with the goal of encouraging more technical courses that are traditionally visually-based to consider possible ways to becoming more accessible.

Authors
Download paper (1.76 MB)

Are you a researcher? Would you like to cite this paper? Visit the ASEE document repository at peer.asee.org for more tools and easy citations.