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Enhanced Training through Interactive Visualization of Training Objectives and Models
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Skövde Artificial Intelligence Lab (SAIL))ORCID iD: 0000-0003-2900-9335
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. Combitech AB. (Skövde Artificial Intelligence Lab (SAIL))
University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre. (Interaction Lab)ORCID iD: 0000-0001-6310-346X
Saab Training Systems, Saab AB, Huskvarna, Jönköping, Sweden.
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2016 (English)In: Proceedings of the STO-MP-MSG-143, Ready for the Predictable, Prepared for the Unexpected: M&S for Collective Defence in Hybrid Environments and Hybrid Conflicts, NATO Science & Technology Organization (STO) , 2016Conference paper, (Refereed)
Abstract [en]

Military forces operate in complex and dynamic environments [1] where bad decisions might have fatal consequences. A key ability of the commander, team and individual warfighter is to quickly adapt to novel situations. Live, Virtual and Constructive training environments all provide elements of best practices for this type of training. However, many of the virtual training are designed without thorough consideration of the effectiveness and efficiency of embedded instructional strategies [2], and without considering the cognitive capabilities and limitations of trainees. As highlighted recently by Stacy and Freeman [3], large military training exercises require a significant commitment of resources, and to net a return on that investment, training scenarios for these events should systematically address well-specified training objectives, even if they often, do not.

In order to overcome these shortcomings with both Live and Virtual training systems and following our previous work [4,5,6], this paper presents a design solution for a proof-of-concept prototype that visualizes and manages training objectives and performance measures, at individual and collective levels. To illustrate its functionality we use real-world data from Live training exercises. Finally, this paper discusses how to learn from previous training experiences using data mining methods in order to build training models to provide instructional personalized feedback to trainees.

Place, publisher, year, edition, pages
NATO Science & Technology Organization (STO) , 2016.
National Category
Computer Science
Research subject
Technology
Identifiers
URN: urn:nbn:se:his:diva-13129DOI: 10.14339/STO-MP-MSG-143ISBN: 978-92-837-2060-7 OAI: oai:DiVA.org:his-13129DiVA: diva2:1048976
Conference
2016 NATO Modelling & Simulation Group (NMSG) Symposium, Bucharest, Romania, October 20-21, 2016
Projects
NOVA 20140294 (Knowledge Foundation)
Funder
Knowledge Foundation, 20140294
Available from: 2016-11-22 Created: 2016-11-22 Last updated: 2017-01-20Bibliographically approved

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Riveiro, MariaGustavsson, Per M.Lebram, Mikael
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
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  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
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  • asciidoc
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