跨模式深度学习增强混合现实加速建筑技能从专家到学生的转移
Chenyu Wang1,2, Meng Zhu2, Sharifah Akman Syed Zakaria3
1School of Civil Engineering, University Sains Malaysia, Trankarian Nibong Tebal, 100032, Penang, Malaysia.
Scientific reports
|October 2, 2025
概括
本研究介绍了一个混合现实培训系统,使用跨模式深度学习来将专家的默契知识传递给建筑学生. 这种新的方法增强了建筑教育中的技能获取,准确性和知识保留.
科学领域:
- 建筑教育教育 建筑教育
- 人工智能的人工智能
- 人与计算机的交互
背景情况:
- 传统的建筑培训很难有效地传递隐含的专家知识.
- 经验丰富的从业者的默契知识很难捕捉和传递.
- 劳动力发展和知识保护是行业的关键挑战.
研究的目的:
- 开发一种新的系统,将专家的默认知识传授给建筑学生.
- 解决传统建筑培训方法的局限性.
- 为了提高教育,利用混合现实和跨模式深度学习.
主要方法:
- 开发了一个整合建筑专家多式联运数据收集的框架.
- 利用跨模式深度学习算法来识别和表示关键的知识模式.
- 实施了一个沉浸式的混合现实培训环境.
主要成果:
- 使用该系统的学生表现出32.4%更快的技能学习和22.4%更高的准确性.
- 在知识保留,情境意识和适应性问题解决方面观察到显著的改善.
- 实验验证涉及80名参与者,将系统与传统方法进行比较.
结论:
- 跨模式深度学习可以将专家的默认知识正式化为可学习的表示.
- 混合现实系统为建筑教育提供了一个可扩展的解决方案.
- 该方法有效地解决了建筑行业的劳动力发展和知识保护需求.
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