当前位置: X-MOL 学术Virtual Real. › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Level of immersion affects spatial learning in virtual environments: results of a three-condition within-subjects study with long intersession intervals
Virtual Reality ( IF 4.2 ) Pub Date : 2020-02-28 , DOI: 10.1007/s10055-019-00411-y
Kimberly A. Pollard , Ashley H. Oiknine , Benjamin T. Files , Anne M. Sinatra , Debbie Patton , Mark Ericson , Jerald Thomas , Peter Khooshabeh

Virtual reality and immersive technologies are used in a variety of learning and training applications. However, higher levels of immersion do not always improve learning. The mixed results in the literature may partly arise from the use of between-subjects designs, insufficient time intervals between sessions in within-subjects designs, and/or overreliance on binary comparisons of immersion levels. Our study examined the influence of three levels of audiovisual immersive technology on spatial learning in virtual environments, using a within-subjects design with long intersession intervals. Performance on object recognition and discrimination was improved in the highest immersion condition, whereas performance on directional bearings showed a U-shaped relationship with level of immersion. Examination of our data suggests that these results likely would not have been found had we used a between-subjects design or a binary comparison, thus demonstrating the value of our approach. Results suggest that different levels of immersion may be better suited to more or less cognitively complex types of spatial learning. We discuss challenges and opportunities for future work.



中文翻译:

沉浸程度会影响虚拟环境中的空间学习:三段条件的受试者内部研究的会话间隔较长

虚拟现实和沉浸式技术被用于各种学习和培训应用中。但是,更高的沉浸感并不总是能改善学习。文献中的混合结果可能部分源于对象间设计的使用,对象内设计中各个会话之间的时间间隔不足和/或过度依赖沉浸级别的二进制比较。我们的研究使用了具有较长的会话间隔的对象内部设计,研究了三个级别的视听沉浸式技术对虚拟环境中空间学习的影响。在最高的浸入条件下,物体识别和辨别性能得到了改善,而定向轴承的性能则与浸入程度呈U形关系。检查我们的数据表明,如果我们使用对象间设计或二进制比较,可能不会找到这些结果,从而证明了我们方法的价值。结果表明,不同程度的沉浸感可能更适合于或多或少的认知复杂类型的空间学习。我们讨论未来工作的挑战和机遇。

更新日期:2020-02-28
down
wechat
bug