Abstract

Feedforward is a training technique where people observe themselves perform a new skill to promote rapid learning, commonly implemented via video self-modelling. Avatars provide a unique opportunity to self-model skills an individual's physical self cannot yet perform. We investigated the use of avatars in video-based learning and explore the potential of feedforward learning from self-avatars. Using modern dancing as a skill to learn, we compared the user experience when learning from a human training video and an avatar training video, considering both self-avatars (n=8) and gender-matched generic avatars (n=8). Our results indicate that learning from avatars can improve the user experience over learning from a human in a video, providing attentional and motivational benefits. Furthermore, self-avatars make the training more relatable and immersive than generic avatars. We discuss the implications from this preliminary work, highlighting methodological considerations for feedforward learning from avatars and promising future work.

Original languageEnglish
Title of host publicationCHI 2022 - Extended Abstracts of the 2022 CHI Conference on Human Factors in Computing Systems
PublisherAssociation for Computing Machinery
Pages1-8
Number of pages8
ISBN (Electronic)9781450391566
DOIs
Publication statusPublished - 27 Apr 2022
Event2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022 - Virtual, Online, USA United States
Duration: 30 Apr 20225 May 2022

Publication series

NameConference on Human Factors in Computing Systems - Proceedings

Conference

Conference2022 CHI Conference on Human Factors in Computing Systems, CHI EA 2022
Country/TerritoryUSA United States
CityVirtual, Online
Period30/04/225/05/22

Keywords

  • Avatar Customisation
  • Feedforward
  • Self-Modelling
  • Skills Training

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Graphics and Computer-Aided Design
  • Software

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