虚拟现实
增强现实
培训(气象学)
任务(项目管理)
多样性(控制论)
计算机科学
荟萃分析
培训转移
人机交互
医学
人工智能
知识管理
工程类
物理
系统工程
气象学
内科学
作者
Alexandra D. Kaplan,Jessica Cruit,Mica R. Endsley,Suzanne M. Beers,Ben D. Sawyer,Peter A. Hancock
出处
期刊:Human Factors
[SAGE Publishing]
日期:2020-02-24
卷期号:63 (4): 706-726
被引量:397
标识
DOI:10.1177/0018720820904229
摘要
Objective The objective of this meta-analysis is to explore the presently available, empirical findings on transfer of training from virtual (VR), augmented (AR), and mixed reality (MR) and determine whether such extended reality (XR)-based training is as effective as traditional training methods. Background MR, VR, and AR have already been used as training tools in a variety of domains. However, the question of whether or not these manipulations are effective for training has not been quantitatively and conclusively answered. Evidence shows that, while extended realities can often be time-saving and cost-saving training mechanisms, their efficacy as training tools has been debated. Method The current body of literature was examined and all qualifying articles pertaining to transfer of training from MR, VR, and AR were included in the meta-analysis. Effect sizes were calculated to determine the effects that XR-based factors, trainee-based factors, and task-based factors had on performance measures after XR-based training. Results Results showed that training in XR does not express a different outcome than training in a nonsimulated, control environment. It is equally effective at enhancing performance. Conclusion Across numerous studies in multiple fields, extended realities are as effective of a training mechanism as the commonly accepted methods. The value of XR then lies in providing training in circumstances, which exclude traditional methods, such as situations when danger or cost may make traditional training impossible.
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