Understanding Differences in Human-Robot Teaming Dynamics between Deaf/Hard of Hearing and Hearing Individuals

Dust, A'di; Gonzalez-Lebron, Carola; Connell, Shannon; Singh, S. P.; Bailey, Reynold; Alm, Cecilia Ovesdotter; Heard, Jamison

doi:10.1145/3568294.3580146

Cited by 1 publication

(1 citation statement)

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“…We focused on participants' opinions toward the task load (mental demand, physical demand, temporal pressure) while watching videos with different styles of signmaku. We adapted NASA Task Load Index [31] (Likert scale 1-10), as it has been used to study DHH interactions with technology, e.g., [29,42]. Each participant responded to nine questions, divided equally among three styles (three questions per style).…”

Section: Measurements and Data Analysismentioning

confidence: 99%

Towards Inclusive Video Commenting: Introducing Signmaku for the Deaf and Hard-of-Hearing

Chen,

Cheng,

Situ

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Previous research underscored the potential of danmaku-a textbased commenting feature on videos-in engaging hearing audiences. Yet, for many Deaf and hard-of-hearing (DHH) individuals, American Sign Language (ASL) takes precedence over English. To improve inclusivity, we introduce "Signmaku, " a new commenting mechanism that uses ASL, serving as a sign language counterpart to danmaku. Through a need-finding study (N=12) and a withinsubject experiment (N=20), we evaluated three design styles: real human faces, cartoon-like figures, and robotic representations. The results showed that cartoon-like signmaku not only entertained but also encouraged participants to create and share ASL comments, with fewer privacy concerns compared to the other designs. Conversely, the robotic representations faced challenges in accurately depicting hand movements and facial expressions, resulting in higher cognitive demands on users. Signmaku featuring real human faces elicited the lowest cognitive load and was the most comprehensible among all three types. Our findings offered novel design implications for leveraging generative AI to create signmaku comments, enriching co-learning experiences for DHH individuals.

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Section: Measurements and Data Analysismentioning

confidence: 99%