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2022 Journal Impact Factor - 0.6
2022 CiteScore - 1.7



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ISSN 2083-6473
ISSN 2083-6481 (electronic version)
 

 

 

Editor-in-Chief

Associate Editor
Prof. Tomasz Neumann
 

Published by
TransNav, Faculty of Navigation
Gdynia Maritime University
3, John Paul II Avenue
81-345 Gdynia, POLAND
www http://www.transnav.eu
e-mail transnav@umg.edu.pl
Using Bayesian Networks to Model Competence of Lifeboat Coxswains
Times cited (SCOPUS): 1
ABSTRACT: The assessment of lifeboat coxswain performance in operational scenarios representing offshore emergencies has been prohibitive due to risk. For this reason, human performance in plausible emergencies is difficult to predict due to the limited data that is available. The advent of lifeboat simulation provides a means to practice in weather conditions representative of an offshore emergency. In this paper, we present a methodology to create probabilistic models to study this new problem space using Bayesian Networks (BNs) to formulate a model of competence. We combine expert input and simulator data to create a BN model of the competence of slow-speed maneuvering (SSM). We demonstrate how the model is improved using data collected in an experiment designed to measure performance of coxswains in an emergency scenario. We illustrate how this model can be used to predict performance and diagnose background information about the student. The methodology demonstrates the use of simulation and probabilistic methods to increase domain awareness where limited data is available. We discuss how the methodology can be applied to improve predictions and adapt training using machine learning.
REFERENCES
Billard, R., Smith, J.J.E. (2018). Using simulation to assess performance in emergency lifeboat launches. Proceedings, e Interservice/Industry Training, Simulation, and Education Conference (I/ITSEC). Paper number 19179.
Billard, R., Smith, J., Veitch B., (2019) Assessing lifeboat coxswain training Alternatives using a simulator. The Journal of Navigation, Published online by Cambridge University Press: 19 September 2019.
Billard, R., Musharraf, M., Smith, J., Veitch B., (2020), Using Bayesian methods and simulator data to model lifeboat coxswain performance. WMU Journal of Maritime Affairs. Published May 2020. - doi:10.1007/s13437-020-00204-0
de Klerk, S., Veldkamp, B.P., Eggen, T., (2015). Psychometric analysis of the performance data of simulation-based assessment: A systematic review and a Bayesian network example. Computers & Education 85 (2015), 23-34. - doi:10.1016/j.compedu.2014.12.020
Dempster, A.P., Laird, N.M., Rubin, D.B. (1977), Maximum Likelihood from Incomplete Data via the EM Algorithm. Journal of the Royal Statistical Society. Series B (Methodological), Vol. 39, No. 1. (1977), pp.1-38. - doi:10.1111/j.2517-6161.1977.tb01600.x
Groth K., Smith, C., Swiler, L. (2014). A Bayesian method for using simulator data to enhance human error probabilities assigned by existing HRA methods. Reliability and System Safety 128 (2014), 32-40 - doi:10.1016/j.ress.2014.03.010
International Maritime Organization., & International Conference on Training and Certification of Seafarers (2010). STCW including 2010 Manila Amendments, 2017 Edition.
International Maritime Organization. (2014). International Convention for the Safety of Life at Sea (SOLAS), Consolidated Edition. London: International Maritime Organization.
Käser, T., Klingler, S., Schwing, A., Gross, M. (2017). Dynamic Bayesian Networks for student modeling. IEEE Transactions on Learning Technologies, Vol. 10, No. 4. Oct.-Dec. 1 2017. - doi:10.1109/TLT.2017.2689017
Klein, G., (2008), Naturalistic decision making. Human Factors: The Journal of Human Factors and Ergonomic Society, 50(3), 456-460. - doi:10.1518/001872008X288385
McClernon, C. K., McCauley, M. E., O’Connor, P. E., & Warm, J. S. (2011). Stress training improves performance during a stressful flight. Human Factors: The Journal of the Human Factors and Ergonomics Society, 53(3), 207-218. - doi:10.1177/0018720811405317
Millán, E., Perez-De-La-Cruz, J.L., (2002). A Bayesian diagnostic algorithm for student modeling and its evaluation. User Modeling and User-Adapted Interaction 12: 281-330, Kluwer Academic Publishers, Netherlands - doi:10.1023/A:1015027822614
Millán , E., Loboda, T., Perez-de-la-Cruz, J.L. (2010). Bayesian networks for student model engineering. Computers and Education, 55, 1663-1683 - doi:10.1016/j.compedu.2010.07.010
Mislevy, R. J., Almond, R. G., & Lukas, J. (2004). A brief introduction to evidence-centered design. CSE technical Report. Los Angeles: The National Center for Research on Evaluation, Standards, and Student Testing (CRESST). Retrieved from http://www.cse.ucla.edu/products/reports/r632.pdf. - doi:10.1037/e646212011-001
Sellberg, C. (2017). Simulators in bridge operations training and assessment: a systematic review and qualitative synthesis. WMU Journal of Maritime Affairs, 16(2), 247-263. - doi:10.1007/s13437-016-0114-8
Stefanidis, D., Korndorffer, J.R., Markley, S., Sierra, R., Heniford, B.T., & Scott, D.J. (2007). Closing the gap in operative performance between novices and experts: does harder mean better for laparoscopic simulator training? Journal of the American College of Surgeons, 205(2), 307-313. - doi:10.1016/j.jamcollsurg.2007.02.080
Weinert, F. E. (2001): Competencies and Key Competencies: Educational Perspective. International Encyclopedia of the Social and Behavioral Sciences, vol. 4, Elsevier, 2433–2436. - doi:10.1016/B0-08-043076-7/02384-6
Citation note:
Billard R., Smith J., Masharraf M., Veitch B.: Using Bayesian Networks to Model Competence of Lifeboat Coxswains. TransNav, the International Journal on Marine Navigation and Safety of Sea Transportation, Vol. 14, No. 3, doi:10.12716/1001.14.03.09, pp. 585-594, 2020
Authors in other databases:
Jennifer Smith:
Mashrura Masharraf:

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