Adaptive Immersive VR Training Based on Performance and Self-Efficacy
| Autor: | Lasse F. Lui, Unnikrishnan Radhakrishnan, Francesco Chinello, Konstantinos Koumaditis |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Frandsen, L L, Radhakrishnan, U, Chinello, F & Koumaditis, K 2023, Adaptive Immersive VR Training Based on Performance and Self-Efficacy . in Proceedings-2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023 . IEEE, pp. 25-29, 2023 IEEE Conference on Virtual Reality and 3D User Interfaces Abstracts and Workshops, VRW 2023, Shanghai, China, 25/03/2023 . https://doi.org/10.1109/VRW58643.2023.00012 |
| DOI: | 10.1109/VRW58643.2023.00012 |
| Popis: | Effective training requires that the training experience fits the ability and confidence (i.e., self-efficacy) of the trainee. Specifically, the individual's self-efficacy should ideally be slightly higher than the difficulty of a given task. A significant benefit of immersive virtual reality (IVR) is the potential to utilize measures of trainee behavior to continuously adapt the training content to the individual. However, a major challenge involves the identification of relevant measures that can be used to adapt training content in a way that increases training output. The current paper aims to inspire further research on adaptive IVR training by describing the design and development of a study on adaptive IVR training where the use of self-efficacy measures for adaptation marks a point of departure from prior literature. The design of the proposed study is informed by analyses of results from previous IVR studies. |
| Databáze: | OpenAIRE |
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