@inproceedings{10.1145/3517428.3550367,
author = {Hassan, Saad and Lee, Sooyeon and Metaxas, Dimitris and Neidle, Carol and Huenerfauth, Matt},
title = {Understanding ASL Learners’ Preferences for a Sign Language Recording and Automatic Feedback System to Support Self-Study},
year = {2022},
isbn = {9781450392587},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3517428.3550367},
doi = {10.1145/3517428.3550367},
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.},
booktitle = {Proceedings of the 24th International ACM SIGACCESS Conference on Computers and Accessibility},
articleno = {85},
numpages = {5},
keywords = {Sign languages, Language learning, Interface design, Feedback, Education, Automatic feedback, American Sign Language},
location = {Athens, Greece},
series = {ASSETS '22}
}