A QoE evaluation of procedural and example instruction formats for procedure training in augmented reality

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Citation (Scopus)

Abstract

Augmented reality (AR) has significant potential as a training platform. The pedagogical purpose of training is learning or transfer. Learning is the acquisition of an ability to perform a procedure as taught while transfer involves generalising that knowledge to similar procedures in the same domain. Quality of experience (QoE) concerns the fulfilment of the application, system or service user's pragmatic and hedonic needs and expectations. Learning or transfer fulfil the AR trainee's pragmatic needs. Training instructions can be presented in procedural, and example formats. Procedural instructions tell the trainee what to do while examples show the trainee how to do it. These two different instruction formats can influence learning, transfer, and hardware resource availability differently. The AR trainee's hedonic needs and expectations may be influenced by the impact of instruction format resource consumption on system performance. Efficient training efficacy is a design concern for mobile AR training applications. This work aims to inform AR training application design by evaluating the influence of procedural and example instruction formats on AR trainee QoE. In this demo, an AR GoCube™ solver training application will be exhibited on the state-of-the-art Hololens 2 (HL2) mixed reality (MR) headset. This AR training app is part of a test framework that will be used in a between groups study to evaluate the influence of text-based and animated 3D model instruction formats on AR trainee QoE. This framework will record the trainee's physiological ratings, eye gaze features and facial expressions. Learning will be evaluated in a post-training recall phase while transfer will be evaluated using a pre and post training comparison of mental rotation skills. Application profiling code will monitor AR headset resource consumption.

Original languageEnglish
Title of host publicationMMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
PublisherAssociation for Computing Machinery, Inc
Pages287-292
Number of pages6
ISBN (Electronic)9781450392839
DOIs
Publication statusPublished - 14 Jun 2022
Event13th ACM Multimedia Systems Conference, MMSys 2022 - Athlone, Ireland
Duration: 14 Jun 202217 Jun 2022

Publication series

NameMMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference

Conference

Conference13th ACM Multimedia Systems Conference, MMSys 2022
Country/TerritoryIreland
CityAthlone
Period14/06/2217/06/22

Keywords

  • Augmented reality
  • cognitive load
  • eye gaze
  • learning
  • memory
  • micro facial expressions
  • quality of experience
  • training

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