Observational Learning for Psychomotor Skills Training in Virtual Environments

Student thesis: Doctoral ThesisDoctor of Engineering (EngD)

Abstract

Training psychomotor skills, whether it be sports, leisure, or work-related activities, represents one of the largest use cases of virtual reality (VR). However, the theoretical grounding of the instructional design and effectiveness of virtual training is limited. Imitation is fundamental to the first stage of learning psychomotor skills, yet observational learning has been under explored in VR training, which has almost exclusively used active `hands-on' training. Furthermore, avatars offer a unique and under explored opportunity to maximise the potential effectiveness of observational learning through learner-model similarity. This thesis explores the application of observational learning in virtual environments, to understand whether avatars can effectively demonstrate psychomotor skills. We explore how avatar similarity affects learning and finally how a combination of observational and `hands-on' virtual experiences compares to a purely active training approach in VR. In two studies we show that avatars can be used effectively to model gross psychomotor skills and that avatar similarity can positively affect learning in this context. In a third study, we interview stakeholders to inform the design of active VR training for fine psychomotor skills and use this to understand the relative efficacy of including observational learning. We reveal that both active and observational approaches are efficacious, and that observational learning can enhance the transferability of skills learned virtually. However, avatar similarity does not positively influence the learning of fine psychomotor skills. This has important implications for how we approach virtual training in the future to maximise its effectiveness. By investigating three levels of avatar similarity, we contribute new insights into the relationship between avatar similarity and learning and identify the technology used, the type of task being trained, and individual factors contributing to these effects. We deliver evidence-based design guidelines and consider how user-avatar characteristics could be influential in training outcomes and virtual experiences generally.
Date of Award2 Oct 2024
Original languageEnglish
Awarding Institution
  • University of Bath
SupervisorChristof Lutteroth (Supervisor), Christopher Clarke (Supervisor), Michael Proulx (Supervisor) & Jeremy Dalton (Supervisor)

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