对位于同一地点、现实生活中的物理学习空间进行自动分析的技术:我们现在在哪里?

Y. H. V. Chua, J. Dauwels, S. Tan
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引用次数: 32

摘要

本文的动机源于这样一个事实,即研究人员和实践者对开发捕捉、建模和分析发生在计算机学习环境之外的学习和教学经验的技术越来越感兴趣。在本文中,我们回顾了一些工具和技术的案例研究,这些工具和技术用于收集和分析教育环境中的数据,量化学习和教学过程,并以自动化的方式支持学习和教学评估。我们专注于利用来自物理空间的信息和数据以及/或集成物理和数字空间收集的数据的管道。我们的综述揭示了物理课堂分析的一个有前途的领域。我们描述了一些趋势,并提出了潜在的未来方向。具体来说,更多的研究应该面向a)在物理学习环境中可部署和可持续的数据收集设置,b)教师评估,c)开发反馈和可视化系统,d)促进模型在人群中的包容性和普遍性。
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Technologies for automated analysis of co-located, real-life, physical learning spaces: Where are we now?
The motivation for this paper is derived from the fact that there has been increasing interest among researchers and practitioners in developing technologies that capture, model and analyze learning and teaching experiences that take place beyond computer-based learning environments. In this paper, we review case studies of tools and technologies developed to collect and analyze data in educational settings, quantify learning and teaching processes and support assessment of learning and teaching in an automated fashion. We focus on pipelines that leverage information and data harnessed from physical spaces and/or integrates collected data across physical and digital spaces. Our review reveals a promising field of physical classroom analysis. We describe some trends and suggest potential future directions. Specifically, more research should be geared towards a) deployable and sustainable data collection set-ups in physical learning environments, b) teacher assessment, c) developing feedback and visualization systems and d) promoting inclusivity and generalizability of models across populations.
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