Pilot Study Using Decision Trees to Diagnose the Efficacy of Virtual Offshore Egress Training

Abstract

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.