TY - GEN
T1 - Understanding Challenges and Opportunities of Technology-Supported Sign Language Learning
AU - Faltaous, Sarah
AU - Winkler, Torben
AU - Schneegass, Christina
AU - Gruenefeld, Uwe
AU - Schneegass, Stefan
PY - 2022
Y1 - 2022
N2 - Around 466 million people in the world live with hearing loss, with many benefiting from sign language as a mean of communication. Through advancements in technology-supported learning, autodidactic acquisition of sign languages, e.g., American Sign Language (ASL), has become possible. However, little is known about the best practices for teaching signs using technology. This work investigates the use of different conditions for teaching ASL signs: audio, visual, electrical muscle stimulation (EMS), and visual combined with EMS. In a user study, we compare participants' accuracy in executing signs, recall ability after a two-week break, and user experience. Our results show that the conditions involving EMS resulted in the best overall user experience. Moreover, ten ASL experts rated the signs performed with visual and EMS combined highest. We conclude our work with the potentials and drawbacks of each condition and present implications that will benefit the design of future learning systems.
AB - Around 466 million people in the world live with hearing loss, with many benefiting from sign language as a mean of communication. Through advancements in technology-supported learning, autodidactic acquisition of sign languages, e.g., American Sign Language (ASL), has become possible. However, little is known about the best practices for teaching signs using technology. This work investigates the use of different conditions for teaching ASL signs: audio, visual, electrical muscle stimulation (EMS), and visual combined with EMS. In a user study, we compare participants' accuracy in executing signs, recall ability after a two-week break, and user experience. Our results show that the conditions involving EMS resulted in the best overall user experience. Moreover, ten ASL experts rated the signs performed with visual and EMS combined highest. We conclude our work with the potentials and drawbacks of each condition and present implications that will benefit the design of future learning systems.
KW - audio
KW - electrical muscle stimulation
KW - Sign language learning
KW - visual
UR - https://www.scopus.com/pages/publications/85128945308
U2 - 10.1145/3519391.3519396
DO - 10.1145/3519391.3519396
M3 - Conference contribution
AN - SCOPUS:85128945308
T3 - ACM International Conference Proceeding Series
SP - 15
EP - 25
BT - Proceedings of Augmented Humans Conference 2022, AHs 2022
PB - Association for Computing Machinery (ACM)
T2 - 2022 Augmented Humans Conference, AHs 2022
Y2 - 13 March 2022 through 15 March 2022
ER -