在虚拟博物馆学习中对行为序列的识别和干预的研究
Xinyi Wu1, Xiaohui Chen1, Jingwen Zhao1
1School of Information Science and Technology, Northeast Normal University, Changchun, Jilin, China.
PloS one
|September 5, 2023
概括
虚拟博物馆为大学生提供沉浸式学习. 辅导和奖励等干预措施显著改善了博物馆沉浸程度低的学生的学习参与度.
科学领域:
- 教育技术的教育技术
- 虚拟学习环境 虚拟学习环境
- 博物馆研究 博物馆研究
背景情况:
- 虚拟博物馆提供灵活的学习机会,超越物理和时间限制.
- 优化虚拟博物馆学习体验的有效干预策略仍未得到充分探索.
- 了解学习者参与对于设计有效的虚拟教育工具至关重要.
研究的目的:
- 在虚拟博物馆环境中调查学习者的行为和参与.
- 根据沉浸程度来识别不同的学习者类型.
- 开发和评估加强虚拟博物馆学习的干预策略.
主要方法:
- 利用模糊的c-集群分析用于虚拟博物馆学习行为识别.
- 采用滞后顺序分析 (LSA) 来分析顺序学习行为.
- 应用随机森林算法来识别干预的关键行为指标.
主要成果:
- 确定了四种学习类型:高,中,低和缺席的博物馆沉浸.
- 针对有针对性的干预,将学习者分为中,高,低和缺席的沉浸组.
- 在实施干预策略后,一些学习者表现出显著的学习状态改善.
结论:
- 虚拟博物馆中的学习者沉浸程度可以被分类和分析.
- 包括支持,指导和游戏化在内的有针对性的干预措施可以有效地增强虚拟博物馆的学习.
- 本研究提供了一个框架,通过数据驱动干预来优化虚拟博物馆教育体验.
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