The Connection Between Constructs and Augmentation Technologies: Measurement Principles Linked to Training and Performance

  • Benjamin Goldberg
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 822)


Forward leaning concepts regarding training and military operations are targeting augmentation technologies to change the interaction landscape. Advancements in virtual reality and augmented reality are providing new means for immersing individuals in realistic experiences and providing access points to data and information previously not easily accessible. While these new interaction modes evolve, measurement techniques are critical in understanding how best to apply these technologies. It is also important to investigate ways measurement techniques can influence the application of these technologies. In this paper, we present a high level overview of augmentation related research focused on human performance dimensions with a discussion on the role of construct measurement to inform and influence their utility. This is followed by a review of a three use cases that are applying the state of the art to advance the practice of training and on the job support tools.


Augmentation Training Human performance Adaptation Measurement 


  1. 1.
    Burford C, Reinerman L, Teo G, Matthews G, McDonnell J, Orvis K, Metevier C et al (2018) Unified multimodal measurement for performance indication research, evaluation, and effectiveness (UMMPIREE): Phase I Report. ARL-TR-8277Google Scholar
  2. 2.
    Carroll M, Kokini C, Champney R, Fuchs S, Sottilare R, Goldberg B (2011) Modeling trainee affective and cognitive state using low cost sensors. In: Proceedings of the interservice/industry training, simulation, and education conference (I/ITSEC), Orlando, FL, USAGoogle Scholar
  3. 3.
    Calvo RA, D’Mello S (2010) Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Trans Affect Comput 1(1):18–37CrossRefGoogle Scholar
  4. 4.
    D’Mello SK, Kory J (2015) A review and meta-analysis of multimodal affect detection systems. ACM Comput Surv (CSUR) 47(3)Google Scholar
  5. 5.
    Picard RW (2009) Future affective technology for autism and emotion communication. Philos Trans R Soc B Biol Sci 364(1535):3575–3584CrossRefGoogle Scholar
  6. 6.
    Picard RW (2016) Automating the recognition of stress and emotion: from lab to real-world impact. IEEE Multimed 23(3):3–7CrossRefGoogle Scholar
  7. 7.
    McDuff D, El Kaliouby R, Cohn JF, Picard RW (2015) Predicting ad liking and purchase intent: large-scale analysis of facial responses to ads. IEEE Trans Affect Comput 6(3):223–235CrossRefGoogle Scholar
  8. 8.
    Goldberg B, Amburn C, Ragusa C, Chen D-W (2018) Modeling expert behavior in support of an adaptive psychomotor training environment: A marksmanship use case. Int J Artif Intell Educ 28(2):194–224CrossRefGoogle Scholar
  9. 9.
    Aleven V, Roll I, McLaren BM, Koedinger KR (2016) Help helps, but only so much: research on help seeking with intelligent tutoring systems. Int J Artif Intell Educ 26(1):205–223CrossRefGoogle Scholar
  10. 10.
    Paquette L, Baker RS (2017) Variations of gaming behaviors across populations of students and across learning environments. In: Proceedings of international conference on artificial intelligence in education, ChinaGoogle Scholar
  11. 11.
    Biddle B (2018) Building a virtual reality pilot: embedding the end user throughout the process. NATO RTG-297 workshop on augmentation technologies, West Pont, NY, USAGoogle Scholar
  12. 12.
  13. 13.

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.U.S. Army Research LaboratoryOrlandoUSA

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