Addressing Gaps in Offshore Emergency Egress Training Using Virtual Environments

  • Jennifer SmithEmail author
  • Mashrura Musharraf
  • Brian Veitch
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1212)


Operators and regulators of offshore and maritime domains should adopt evidence-based safety training to prepare the workforce for emergency egress. This paper uses pedagogical frameworks and data mining tools to identify training gaps in mandatory offshore safety training, and offers evidence-based virtual environment (VE) training solutions. A VE training setting was used as a human behavior laboratory to provide trainees with artificial experience and record their learning progress in the context of evacuating a virtual offshore petroleum platform during a series of credible emergencies. A longitudinal study was conducted to collect data at three critical learning stages: skill acquisition, retention, and transfer. The empirical evidence identified strengths and deficiencies in the VE training. The modeling provided a more comprehensive assessment of the VE training and demonstrated the utility of data-mining tools for future adaptive training applications.


Offshore Emergency Egress Virtual training Decision trees 



The authors acknowledge with gratitude the support of the NSERC/Husky Energy Industrial Research Chair in Safety at Sea.


  1. 1.
    McGaghie, W., Issenberg, S., Barsuk, J., Wayne, D.: A critical review of simulation-based mastery learning with translational outcomes. Med. Educ. 48, 375–385 (2014)CrossRefGoogle Scholar
  2. 2.
    Smith, J., Doody, K., Veitch, B.: Being prepared for emergencies: a virtual environment experiment on the retention and maintenance of egress skills. WMU J. Maritime Aff. 18(3), 425–449 (2019)CrossRefGoogle Scholar
  3. 3.
    Smith, J., Veitch, B.: A better way to train personnel to be safe in emergencies. ASCE-ASME J. Risk Uncertainty Eng. Syst. Part B 5(1), 011003 (2018)CrossRefGoogle Scholar
  4. 4.
    Barnett, S., Ceci, S.: When and where do we apply what we learn? a taxonomy for far transfer. Psychol. Bull. 128, 612–637 (2002)CrossRefGoogle Scholar
  5. 5.
    Ford, J., Baldwin, T., Prasad, J.: Transfer of training: The known and the unknown. Ann. Rev. Organ. Psychol. Organ. Behav. 5, 201–225 (2018)CrossRefGoogle Scholar
  6. 6.
    Han, J., Kamber, M., Pei, J.: Data Mining: Concepts and Techniques. Elsevier, Amsterdam (2012)zbMATHGoogle Scholar
  7. 7.
    Musharraf, M., Smith, J., Khan, F., Veitch, B.: Identifying route selection strategies in offshore emergency situations using decision trees. J. Reliab. Eng. Syst. Saf. 19, 106179 (2020)CrossRefGoogle Scholar
  8. 8.
    Smith, J., Musharraf, M., Veitch, B.: Data informed cognitive modelling of offshore emergency egress behaviour. In: Proceedings of the 2017 International Conference on Cognitive Modeling, Coventry, UK (2017)Google Scholar
  9. 9.
    Doody, K.: Evaluation of performance in emergency response scenarios: a virtual environment skill retention study. Master’s Thesis, Memorial University of Newfoundland (2018)Google Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2020

Authors and Affiliations

  • Jennifer Smith
    • 1
    Email author
  • Mashrura Musharraf
    • 1
  • Brian Veitch
    • 1
  1. 1.Faculty of Engineering and Applied ScienceMemorial University of NewfoundlandSt. John’sCanada

Personalised recommendations