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    Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10259/7324

    Título
    Countering the Novelty Effect: A Tutorial for Immersive Virtual Reality Learning Environments
    Autor
    Miguel Alonso, InésAutoridad UBU Orcid
    Rodríguez García, BrunoAutoridad UBU Orcid
    Checa Cruz, DavidAutoridad UBU Orcid
    Bustillo Iglesias, AndrésAutoridad UBU Orcid
    Publicado en
    Applied sciences. 2023, V. 13, n. 1, 593
    Editorial
    MDPI
    Fecha de publicación
    2023-01
    DOI
    10.3390/app13010593
    Résumé
    Immersive Virtual Reality (iVR) is a new technology, the novelty effect of which can reduce the enjoyment of iVR experiences and, especially, learning achievements when presented in the classroom; an effect that the interactive tutorial proposed in this research can help overcome. Its increasingly complex levels are designed on the basis of Mayer’s Cognitive Theory of Multimedia Learning, so that users can quickly gain familiarity with the iVR environment. The tutorial was included in an iVR learning experience for its validation with 65 users. It was a success, according to the user satisfaction and tutorial usability survey. First, it gained very high ratings for satisfaction, engagement, and immersion. Second, high skill rates suggested that it helped users to gain familiarity with controllers. Finally, a medium-high value for flow pointed to major concerns related to skill and challenges with this sort of iVR experience. A few cases of cybersickness also arose. The survey showed that only intense cybersickness levels significantly limited performance and enjoyment; low levels had no influence on flow and immersion and little influence on skill, presence, and engagement, greatly reducing the benefits of the tutorial, despite which it remained useful.
    Palabras clave
    Cybersickness
    Education
    Tutorial
    Novelty effect
    Virtual reality
    Materia
    Informática
    Computer science
    URI
    http://hdl.handle.net/10259/7324
    Versión del editor
    https://doi.org/10.3390/app13010593
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    Miguel-as_2023.pdf
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