Abstract
In most technical domains, non-technical skills have an influence on a worker’s performance. Studies have shown that these skills are most influential during critical situations, where usual technical procedures cannot be successfully applied. This article describes the challenges raised by the diagnosis of non-technical skills during critical situations inside a virtual environment, and presents the first steps of this diagnosis task, namely the evaluation of a learner’s perceptual and gestural performance using a neural network.
This research was supported by the MacCoy-Critical project (ANR-14-CE24-0021).
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Bourrier, Y., Jambon, F., Garbay, C., Luengo, V. (2017). A Multi-layered Architecture for Analysis of Non-technical-Skills in Critical Situations. In: André, E., Baker, R., Hu, X., Rodrigo, M., du Boulay, B. (eds) Artificial Intelligence in Education. AIED 2017. Lecture Notes in Computer Science(), vol 10331. Springer, Cham. https://doi.org/10.1007/978-3-319-61425-0_41
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DOI: https://doi.org/10.1007/978-3-319-61425-0_41
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