在虚拟现实教育中解码学习者的行为:从认识网络分析和差异序列采矿的见解
IEEE transactions on visualization and computer graphics
|March 10, 2025
概括
这项研究分析了沉浸式虚拟现实学习环境 (VRLEs) 中的学习者行为. 高绩效的人表现出结构化,代式的学习模式,与低绩效的人不同.
科学领域:
- 教育技术的教育技术
- 人与计算机的交互
- 认知科学 认知科学
背景情况:
- 沉浸式虚拟现实 (I-VR) 提供了增强的教育体验,但理解这些环境中的学习者行为仍未得到充分研究.
- 现有的研究往往侧重于学习成果和与传统方法的比较,忽视了虚拟现实学习环境 (VRLEs) 中的行为动态.
- 在分析VRLEs中的特定学习者行为和交互模式与绩效的相关性方面存在差距.
研究的目的:
- 通过记录和分析交互数据来调查VRLE中的学习者行为.
- 在VRLE背景下识别高和低绩效学生之间的行为差异.
- 为开发适应性VR学习内容和个性化脚手架提供见解.
主要方法:
- 开发了一种交互行为数据 (IBD) 记录机制,以捕捉VRLE中的学习者交互.
- 收集了30名计算机科学本科生学习电磁感应概念的数据.
- 利用经验网络分析 (ENA) 和差异序列挖掘 (DSM) 来分析记录的行为并识别与性能相关的行为模式.
主要成果:
- 高绩效者在VRLE中展示了结构化,代的实验和评估模式.
- 低表现的人与高表现的人相比,表现不佳的人表现出不那么专注的探索模式.
- ENA和DSM在高绩效和低绩效者之间的突出行动,同时发生的行动和行动模式中发现了显著的差异.
结论:
- 在VRLEs中的学习者行为显著影响学习成果.
- 独特的行为模式在VR学习中区分高绩效和低绩效的学生.
- 研究结果支持开发适应性VR学习内容,以适应个体学习者行为,以提高教育效率.
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