TY - GEN
T1 - A QoE evaluation of procedural and example instruction formats for procedure training in augmented reality
AU - Hynes, Eoghan
AU - Flynn, Ronan
AU - Lee, Brian
AU - Murray, Niall
N1 - Publisher Copyright:
© 2022 ACM.
PY - 2022/6/14
Y1 - 2022/6/14
N2 - 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.
AB - 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.
KW - Augmented reality
KW - cognitive load
KW - eye gaze
KW - learning
KW - memory
KW - micro facial expressions
KW - quality of experience
KW - training
UR - http://www.scopus.com/inward/record.url?scp=85137142093&partnerID=8YFLogxK
U2 - 10.1145/3524273.3532899
DO - 10.1145/3524273.3532899
M3 - Conference contribution
AN - SCOPUS:85137142093
T3 - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
SP - 287
EP - 292
BT - MMSys 2022 - Proceedings of the 13th ACM Multimedia Systems Conference
PB - Association for Computing Machinery, Inc
T2 - 13th ACM Multimedia Systems Conference, MMSys 2022
Y2 - 14 June 2022 through 17 June 2022
ER -