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An Exploration of High-Fidelity Virtual Training Simulators on Learners' Self-Efficacy: A Mixed Methods Study
Holbrook, Heather Anne
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An Exploration of High-Fidelity Virtual Training Simulators on Learnersâ Self-Efficacy: A Mixed Methods Study Heather A. Holbrook Abstract In this world of fast-paced learning, training agencies often require their learners to acquire the knowledge and skills needed for a job at an expedited rate. Because of this rapid form of training, learners are sometimes uncertain about their abilities to execute task-based performances. This uncertainty can lead to a decrease in learnersâ self-efficacy on expected task performance. In order to help with this training, trainers are using a variety of simulations and simulators to provide learnersâ valuable and necessary training experiences. This mixed methods study explored the influence of high-fidelity virtual training simulators on learnersâ self-efficacy. It used pre- and post-simulation-use surveys that combined general self-efficacy questions (Schwarzer & Jerusalem, 1995) and task-specific self-efficacy questions (Bandura, 1977, 1997, 2006; Bandura, Adams, Hardy, & Howells, 1980). This study had a sample size of 18 participants. It was assumed that the intent of providing learners with the vital experience needed to perform specific tasks in a high-fidelity virtual training simulator was to increase their self-efficacy on task-specific criteria. Instead, through surveys, observations, and interviews, the research revealed a decrease in learnersâ self-efficacy due to heightened emotional arousal stemming from the learnersâ experiences with the level of realism the simulator provide, as well as with breakdowns within the simulator. The breakdowns and the realism were the most influential aspects that influenced self-efficacy in this study. The significance of these findings shows that despite learners wanting to use high-fidelity virtual training simulators, improperly functioning simulators can negatively influence learnersâ self-efficacy in task-based performances.
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