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For the offshore energy industry, virtual environment technology can enhance conventional training by teaching basic offshore safety protocols such as onboard familiarization and emergency evacuation. Virtual environments have the added benefit of being used to investigate the impact of different training approaches on competence. This pilot study uses decision tree modeling to examine the efficacy of two pedagogical approaches, simulation-based mastery learning (SBML) and lecture-based training (LBT), in a virtual environment. Decision trees are an inductive reasoning approach that can be used to identify learners' egress strategies in offshore emergencies after training. The efficacy of the virtual training is evaluated in three ways: 1) analyzing participants' performance scores in test scenarios; 2) comparing the decision tree depiction of participant's understanding of emergency egress to the intended learning objectives; and 3) comparing the decision strategies developed under a different pedagogical approach. A comparison of the resulting decision trees from the SBML training with trees generated from the LBT showed that the different training methods influenced the participants' egress strategies. The SBML approach resulted in concise decision trees and better route selection strategies when compared to the LBT training. This pilot study demonstrates the diagnostic capabilities of decision trees as training assessment tools and recommends integrating decision trees into virtual training to better support the learning needs of individuals and deliver adaptive training scenarios.

To prepare personnel for offshore emergencies, safety training should focus on transferability. Virtual environment (VE) training is designed to support the transfer of acquired egress skills to novel offshore emergencies. Decision trees (DT) are useful tools to evaluate training transfer. DTs use performance data collected during VE training to model participants' behavioural patterns. A DT that reflects ideal behaviour is used as a benchmark to compare the compliance of the participants' data-informed behavioural patterns. Employing the diagnostic and predictive capabilities of DTs can indicate whether a person is capable of responding to a wide variety of emergencies using inference. This paper demonstrates the use of DTs as a curriculum design and assessment tool to determine if the VE training adequately prepared participants to transfer their egress skills to new emergencies in the same virtual setting.

The Functional Resonance Analysis Method (FRAM) is a novel research methodology that uses qualitative data collection methods to map and model complex healthcare processes by identifying and depicting the cumulative activities required to produce an outcome. The FRAM aims to identify the variability that can emerge in a process when healthcare activities are performed under dynamic conditions. With this knowledge, health care system design, safety, and quality improvement recommendations can be developed with a greater understanding of everyday process functionality. Researchers interested in using the FRAM require both an understanding of the methodology itself, as well as an understanding of how to effectively plan and conduct qualitative research. The purpose of this paper is to provide practical guidance to researchers on planning and operationalizing qualitative data collection and analysis methods to inform the building of a FRAM model. A combination of literature and practical experience will be used to examine and suggest appropriate ways for researchers to carry out this work. [ABSTRACT FROM AUTHOR]