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Mining students' digital footprints to enhance simulator training

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DOITrouver le DOI : https://doi.org/10.54941/ahfe1003854
AuteurRechercher : ; Rechercher : Identifiant ORCID : https://orcid.org/0000-0001-8423-0572; Rechercher : ; Rechercher :
FormatTexte, Article
Conférence14th International Conference on Applied Human Factors and Ergonomics (AHFE 2023), July 20-24, 2023, San Francisco, California, USA
Sujetdata mining; simulator training; offshore emergency; logistic regression
Résumé
Offshore emergencies are dynamic in nature and personnel on board are challenged with stress, uncertainty, and complexity. The lack of knowledge about how these factors affect peoples’ egress performance in emergencies undermines organizations’ ability to manage safety. A solid understanding of the impact of these factors on egress performance can help design better training programs. This paper investigates the effect of three factors: hazard proximity (to reflect stress), information quality (to reflect uncertainty), and situation familiarity (to reflect complexity) on seven different performance criteria of emergency egress. The performance criteria include mustering in time, maintaining a safe pace, keeping fire doors closed, avoiding interaction with hazards, reporting at the correct muster location, selecting appropriate personal protective (PP) gear, and selecting an efficient egress route. The investigation is driven by digital footprints of individuals performing in a training simulator under the influence of these factors. Besides measuring the impact of the factors, the data is also used to mine correlation among the performance criteria themselves (i.e. if a participant selects an efficient egress route, does that mean s/he will muster in time as well?). Finally, the data were analyzed to investigate if task performance can be modelled as a function of the factors that were identified to have a significant impact. Plotting the relationship between a performance criteria (outcome variables) and the significant factors (predictors) helps visualize the plausibility of a predictive model. In cases where predictive models were deemed plausible, binary logistic regression models were used as all performance criteria were categorical and dichotomous. The models predicted the probability of success in the performance criteria under the pressure of an emergency. The predictions are an indication of the degree of emergency preparedness of participants subsequent to training. A high probability of success in each criterion increases confidence in the training program. A low probability of success in one or more criteria highlights the gap in the training. The corresponding logistic regression model can provide direction on how the gap can be addressed.
Date de publication
Maison d’éditionAHFE International
Licence
Dans
  • Human Factors in Transportation (2023) : 686–697.
Série
  • AHFE International 95.
Langueanglais
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Identificateur de l’enregistrementd0b277ef-8712-4ff8-aa7e-7c80a3f70169
Enregistrement créé2024-06-14
Enregistrement modifié2024-06-14

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