@inproceedings{8f6bfca9c4c142948802126edf4da8d6,
title = "Understanding ASL Learners' Preferences for a Sign Language Recording and Automatic Feedback System to Support Self-Study",
abstract = "Advancements in AI will soon enable tools for providing automatic feedback to American Sign Language (ASL) learners on some aspects of their signing, but there is a need to understand their preferences for submitting videos and receiving feedback. Ten participants in our study were asked to record a few sentences in ASL using software we designed, and we provided manually curated feedback on one sentence in a manner that simulates the output of a future automatic feedback system. Participants responded to interview questions and a questionnaire eliciting their impressions of the prototype. Our initial findings provide guidance to future designers of automatic feedback systems for ASL learners.",
keywords = "American Sign Language, Automatic feedback, Education, Feedback, Interface design, Language learning, Sign languages",
author = "Saad Hassan and Sooyeon Lee and Dimitris Metaxas and Carol Neidle and Matt Huenerfauth",
note = "Publisher Copyright: {\textcopyright} 2022 Owner/Author.; 24th International ACM SIGACCESS Conference on Computers and Accessibility, ASSETS 2022 ; Conference date: 23-10-2022 Through 26-10-2022",
year = "2022",
month = oct,
day = "22",
doi = "10.1145/3517428.3550367",
language = "English (US)",
series = "ASSETS 2022 - Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility",
publisher = "Association for Computing Machinery, Inc",
booktitle = "ASSETS 2022 - Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility",
}