Feedback strategies for embodied agents to enhance sign language vocabulary learning

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Abstract

When learning sign language, feedback on accuracy is critical to vocabulary acquisition. When designing technologies to provide such visual feedback, we need to research effective ways to identify errors and present meaningful and effective feedback to learners. Motion capture technologies provide new opportunities to enhance sign language learning experiences through the presentation of visual feedback that indicates the accuracy of the signs made by learners. We designed, developed, and evaluated an embodied agent-based system for learning the location and gross motor movements of sign language vocabulary. The system presents a sign, tracks the learner's attempts at a sign, and provides visual feedback to the learner on their errors. We compared five different types of visual feedback, and in a study involving 51 participants we established that learners preferred visual feedback where their attempts at a sign were shown concurrently with the movements of the instructor with or without explicit corrections.

Original languageEnglish
Title of host publicationProceedings of the 20th ACM International Conference on Intelligent Virtual Agents, IVA 2020
EditorsHannes Vilhjalmsson, Pedro Sequeira, Emily S. Cross
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450375863
DOIs
Publication statusPublished - 2020
EventIntelligent Virtual Agents 2020 - Virtual, United Kingdom
Duration: 20 Oct 202022 Oct 2020
Conference number: 20th
https://dl.acm.org/doi/proceedings/10.1145/3383652 (proceedings)
https://iva2020.gla.ac.uk (Website)

Conference

ConferenceIntelligent Virtual Agents 2020
Abbreviated titleIVA'20
CountryUnited Kingdom
CityVirtual
Period20/10/2022/10/20
Internet address

Keywords

  • accessibility
  • HCI
  • intelligent virtual agent
  • motor skill
  • sign language
  • visual feedback
  • visualization

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