Beyond English: Designing Culturally-Aligned ASL Questionnaires

TL;DR

In the world of Human-Computer Interaction (HCI), the "default" user is often assumed to be a reader of written languages like English. This paper challenges that assumption, detailing the methodology for creating research questionnaires in American Sign Language (ASL). By addressing the nuances of video production, signer authorship, and cultural identity, the authors provide a blueprint for respect-based, accessible research within the Deaf community.

The "English Prerequisite" Problem

For many Deaf adults in the U.S., ASL is their primary language, yet almost all digital research tools—Qualtrics, SurveyMonkey, Google Forms—are optimized for text. This creates a "second language" barrier.

The problem isn't just about translation; it's about cultural and technical friction:

• Authorship Paradox: Unlike written text, a signer’s face is always visible. Does the participant see the signer as the "author" or just a "mouthpiece"?
• Privacy Constraints: While an English author can remain anonymous, a signer's identity (race, gender, and regional signs) is immediately public, impacting their privacy and the study's bias.
• Technical Gaps: Survey platforms don't easily support video questions and video answers, often forcing users back into text-based English.

Methodology: The Core Dimensions of ASL Research

The authors break down the development of ASL questionnaires into several critical dimensions that go beyond standard HCI practices.

1. Authorship and Representation

In ASL, the signer is "connected" to the content in a way a text designer is not. The paper recommends that all co-authors and contributors be acknowledged at the start of the video to prevent the signer from being viewed as the sole author. This is a vital cultural consideration to ensure the research is seen as a collective institutional effort.

2. Community-Specific Questioning

The team refined demographic questions to include terms specific to the community's identity: Deaf, Hard of Hearing, Late Deafened, and DeafBlind. They emphasized avoiding "pathologizing" language—treating deafness as a cultural identity rather than a medical condition to be fixed.

3. Remote Production Framework

Due to the pandemic, the team shifted to a distributed video production model. They established a rigorous home-studio protocol: The visual documentation of the ASSET '20 poster highlights the collaborative nature of the project.

Key Home Studio Equipment:

• iPhone 11 Max Pro
• 10’ x 10’ Grey backdrop (consistency is key for visual clarity)
• Studio lights with dimmers (shadows can obscure sign nuances)
• Remote triggers for filming to ensure the signer is centered.

The Case Against Current Avatars

A common suggestion to solve signer privacy issues is to use Signing Avatars. However, the authors explicitly state that current avatars are not recommended.

• The "Nuance" Gap: ASL relies on micro-expressions of the face, body tilt, and hand fluidity.
• Community Distrust: Most Deaf community members find current avatars lack the linguistic "finesse" required for clear communication, making them feel unnatural and distracting.

Experimental Challenges: Collaboration over Video

The study also highlighted how current collaboration tools (like Zoom) are fundamentally "Audio-Centric."

• The "Unmute" Obstruction: Repeated alerts to "unmute" interfered with signers.
• Layout Frustrations: The inability to pin interpreters in specific grid locations (like the center) makes it difficult for Deaf researchers to see the interpreter and the hearing team members simultaneously.

Critical Analysis & Future Outlook

Takeaway

This work is a call to action for the HCI community to move beyond "text-first" design. For research to be valid in the Deaf community, the instrument must be as fluent as the participant.

Limitations

A major bottleneck remains the collection of data. While the authors solved the "delivery" of questions via video, the "response" side still largely relies on multiple-choice options. A true breakthrough would involve platforms that allow for seamless ASL video uploads by participants and automated tagging/analysis of those videos.

Future Work

The next frontier is the development of robust, video-centric survey platforms that treat video as a first-class data type, allowing for the same level of qualitative analysis we currently apply to text.

Citation: Rachel Boll, Shruti Mahajan, Jeanne Reis, and Erin T. Solovey. 2020. Creating questionnaires that align with ASL linguistic principles and cultural practices within the Deaf community. In ASSETS ‘20.