Yue-Ting Siu

TL;DR: Two methods of employing novice Web workers to author descriptions of science, technology, engineering, and mathematics images to make them accessible to individuals with visual and print-reading disabilities are compared.

TL;DR: In this paper, the authors argue for the utility of two additional frameworks to enhance UDL efforts: enactivism, a cognitive-sciences view of learning, knowing, and reasoning as modal activity; and ethnomethodological conversation analysis, which investigates participants' multimodal methods for coordinating action and meaning.

TL;DR: The HILML approach facilitates human-machine collaboration to produce high quality video descriptions while keeping a low barrier to entry for volunteer describers and was significantly faster and easier to use for first-time video describers compared to a human-only control condition with no machine learning assistance.

TL;DR: An instrument that measures the assistive technology proficiency of teachers of students with visual impairments and their identification with a community of practice that values technology is presented.

TL;DR: A Human-in-the-Loop Machine Learning (HILML) approach to video description is developed by automating video text generation and scene segmentation while allowing humans to edit the output.