Sofia Seinfeld;Filippo Gabriele Pratticó;Chiara De Giorgi;Fabrizio Lamberti
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引用次数: 0
Abstract
Swapping visual perspective in virtual reality (VR) provides a unique means for embodying different virtual bodies and for self-distancing. Moreover, this technology is a powerful tool for experiential learning and for simulating realistic scenarios, with broad potential in the training of soft skills. However, there is scarce knowledge on how perspective swapping in VR might benefit the training of soft skills such as those required in a job interview. This article investigates the impact of virtual body swapping on the self-assessment of verbal and nonverbal communication skills, emotional states, and embodiment in a simulated job interview context. Three main conditions were compared: a baseline condition in which the participants practiced a job interview from the first-person perspective of a virtual interviewee (no swap condition); an external point of view condition where, first, the participants answered questions from the interviewee perspective, but then swap visual perspective to re-experience their responses from a nonembodied point of view (out of body condition); and a condition in which, after answering questions from the interviewee perspective, the participants re-experienced their responses from the embodied perspective of the virtual recruiter (recruiter condition). The experimental results indicated that the effectiveness of the out of body and recruiter conditions was superior to the no swap condition to self-assess the communication styles used during a job interview. Moreover, all the conditions led to a high level of embodiment toward the interviewee avatar when seen from the first-person perspective; in the case of the recruiter condition, the participants also felt embodied in the recruiter avatar. No differences in emotional states were found among conditions, with all sharing a positive valence.
期刊介绍:
The IEEE Transactions on Learning Technologies covers all advances in learning technologies and their applications, including but not limited to the following topics: innovative online learning systems; intelligent tutors; educational games; simulation systems for education and training; collaborative learning tools; learning with mobile devices; wearable devices and interfaces for learning; personalized and adaptive learning systems; tools for formative and summative assessment; tools for learning analytics and educational data mining; ontologies for learning systems; standards and web services that support learning; authoring tools for learning materials; computer support for peer tutoring; learning via computer-mediated inquiry, field, and lab work; social learning techniques; social networks and infrastructures for learning and knowledge sharing; and creation and management of learning objects.