职业应用许多职业中涉及使用复杂工具的人需要知道如何以最低成本实时对这些工具进行故障排除。因此,培训故障排除专业人员是各个职业部门特别是军事部门的关注,航空,发电厂,以及在工业过程中-已经进行了大量投资来创建和使用模拟器来训练故障排除技能。在设计和评估模拟器以进行故障排除时,这篇综述表明,没有单一水平的模拟现实主义-或保真度-在训练中效果最好,保真度的效果取决于受训者的先前技能水平和故障排除系统的类型(电子或机械)。
背景:由于方法上的困难,确定适当水平的模拟保真度的培训一直是研究人员和从业者在人体工程学和人为因素以及模拟教育者的长期问题。目的:我们的目标是了解不同级别的仿真保真度是否以及如何影响培训故障排除专业人员的转移,专注于军事等实践领域,化工厂,和航空。在分析保真度的影响时,我们还评估了两个主持人的潜在影响:学员的先验技能和系统类型(电子和机械)。方法:我们使用定量(随机效应)和定性荟萃分析技术来解决研究问题。为了克服传统的保真度测量和转移问题,我们没有使用定量措施,而是对研究变量进行了定性分类,中等,和高水平。从1960年到现在(2021年)的报告描述了使用在线数据库进行的对照实验,这导致了200份报告,其中25个满足了我们的条件,包括57个实验,有1,481名人类参与者。结果:尽管总体结果有利于使用中高保真模拟器,没有一个低,medium-,或者高保真模拟普遍优越,保真度的影响取决于确定的主持人。保真度对转移有积极的影响,但仅限于具有较高先验技能的学员。仅在电子系统中也观察到相同的效果。在三个忠诚级别中,中等保真度模拟器产生了最高的整体转移,特别是对于先验技能低的学员,和低保真度模拟器导致最低的整体转移。结论:在设计和评估培训故障排除专业人员的模拟器时,只有通过分析重要的主持人,如学员的先验技能和系统类型,才能解决保真度问题。因此,研究人员和从业者应该定义这样的主持人,然后决定关键的设计变量,如保真度。
OCCUPATIONAL APPLICATIONSPeople in many occupations that involve using complex tools need to know how to troubleshoot those tools in real time and with minimum cost. Training troubleshooting professionals is thus a concern for various occupational sectors-particularly the military, aviation, power plant, and in industrial processes-and large investments have been made to create and use simulators that train troubleshooting skills. In the design and evaluation of simulators for troubleshooting, this review shows that no single level of simulation realism-or fidelity-works best in training, and that the effect of fidelity depends on trainees\' prior skill level and the type of troubleshooting system (electronic or mechanical).
Background: Due to methodological difficulties, determining the appropriate level of simulation fidelity for training has been a long-standing problem for researchers and practitioners in ergonomics and human factors as well as simulation educators. Purpose: Our goal was to understand whether and how different levels of simulation fidelity affect transfer for training troubleshooting professionals, with a focus on practice domains such as military, chemical plants, and aviation. In analyzing the effect of fidelity, we also assessed the potential effects of two moderators: trainees’ prior skill and system type (electronic and mechanical). Method: We used quantitative (random effects) and qualitative meta-analytic techniques to address the study questions. To overcome traditional problems in measuring fidelity and transfer, instead of using quantitative measures we conducted a qualitative categorization of study variables into low, medium, and high levels. Reports from 1960 until the present (2021) that described controlled experiments were identified using online databases, which resulted in 200 reports, 25 of which satisfied our conditions and included 57 experiments with 1,481 human participants. Results: Although the overall results favor using medium- to high-fidelity simulators, none of the low-, medium-, or high-fidelity simulations were universally superior, and the effect of fidelity depended on identified moderators. There was a positive effect of fidelity on transfer, but only for trainees with high prior skill. The same effect was also observed only for electronic systems. Of the three level of fidelity, medium-fidelity simulators produced the highest overall transfer, especially for trainees with low prior skill, and the low-fidelity simulators resulted in the lowest overall transfer. Conclusion: In designing and evaluating simulators that train troubleshooting professionals, addressing the fidelity question is only possible by analyzing important moderators such as trainees’ prior skills and system type. Researchers and practitioners should thus define such moderators and then decide on key design variables such as fidelity.