Leveraging multimodal learning analytics to differentiate student learning strategies
Leveraging multimodal learning analytics to differentiate student learning strategies
复制标题
利用多模式学习分析来区分学生的学习策略
DOI：
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复制
发表时间：
2015
期刊：
影响因子：
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通讯作者：
Paulo Blikstein
中科院分区：
文献类型：
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作者：
M. Worsley;Paulo Blikstein
多模态分析已经证明了在研究和模拟几种人与人和人机交互方面的有效性。在本文中，我们探讨了多模态分析在研究复杂学习环境中的作用。我们比较了单模态和多模态；手工和半自动化的方法来检查学生如何在动手，工程设计环境中学习。具体来说，我们比较了来自一项研究（N=20）的人类注释、语音、手势和皮肤电激活数据，其中学生参与了两种不同的实验条件。实验条件已经被证明与学习收益和设计质量的差异有关。因此，本文的一个目标是确定两种实验条件下不同的行为实践，因为这可能有助于我们更好地理解学习干预是如何起作用的。另一个目标是提供如何在复杂环境中进行学习分析研究的示例，并比较相同的算法在处理不同形式的数据时如何提供互补的结果。
Multimodal analysis has had demonstrated effectiveness in studying and modeling several human-human and human-computer interactions. In this paper, we explore the role of multimodal analysis in the service of studying complex learning environments. We compare uni-modal and multimodal; manual and semi-automated methods for examining how students learn in a hands-on, engineering design context. Specifically, we compare human annotations, speech, gesture and electro-dermal activation data from a study (N=20) where student participating in two different experimental conditions. The experimental conditions have already been shown to be associated with differences in learning gains and design quality. Hence, one objective of this paper is to identify the behavioral practices that differed between the two experimental conditions, as this may help us better understand how the learning interventions work. An additional objective is to provide examples of how to conduct learning analytics research in complex environments and compare how the same algorithm, when used with different forms of data can provide complementary results.
DOI：
10.1016/b978-0-08-044894-7.00458-9
发表时间：
2010
期刊：
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影响因子：
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作者：
Lanskey C
通讯作者：
Lanskey C