如何用智能手表补充学习分析?:身体活动、环境情境和学习活动的融合

George-Petru Ciordas-Hertel
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引用次数: 2

摘要

为了获得学习的整体视角,学习分析(LA)的多模态技术基础结构可能是有益的。最近的研究调查了洛杉矶技术基础设施的各个方面。然而,目前还没有研究如何将LA指标与智能手表传感器数据相结合,以检测身体活动和环境背景。传感器数据,如加速度计,经常用于相关工作,以推断特定的行为和环境背景,从而触发及时的干预措施。在这个论文项目中,我们计划使用智能手表传感器数据来探索在野外进行的混合学习课程中学习的进一步指标,例如在家里。这些指标可以在学习过程中用于建议休息时间,或者在学习结束后用于支持学习者的反思过程。我们计划调查以下三个研究问题:(RQ1)如何设计多模态学习分析基础设施来有效地支持实时数据采集和处理?(RQ2)如何使用智能手表传感器数据推断环境背景和身体活动,以补充混合式学习课程的学习分析指标;(RQ3)我们如何将提取的多模态指标与教学干预措施结合起来。RQ1通过结构化文献综述和对洛杉矶基础设施开发商进行11次半结构化访谈进行调查。根据RQ2,我们目前正在设计和实现一个多模式学习分析基础设施,以收集和处理来自智能手表的传感器和体验数据。最后,根据RQ3,将进行一项探索性的实地研究,以提取多模态学习指标,并与学习者和教学专家一起对其进行检查,以制定有效的干预措施。研究人员、教育工作者和学习者可以使用和调整我们的贡献,以获得关于学习者的时间和学习策略的新见解,以及从野外学习课程中获得的物理学习空间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
How to Complement Learning Analytics with Smartwatches?: Fusing Physical Activities, Environmental Context, and Learning Activities
To obtain a holistic perspective on learning, a multimodal technical infrastructure for Learning Analytics (LA) can be beneficial. Recent studies have investigated various aspects of technical LA infrastructure. However, it has not yet been explored how LA indicators can be complemented with Smartwatch sensor data to detect physical activity and the environmental context. Sensor data, such as the accelerometer, are often used in related work to infer a specific behavior and environmental context, thus triggering interventions on a just-in-time basis. In this dissertation project, we plan to use Smartwatch sensor data to explore further indicators for learning from blended learning sessions conducted in-the-wild, e.g., at home. Such indicators could be used within learning sessions to suggest breaks, or afterward to support learners in reflection processes. We plan to investigate the following three research questions: (RQ1) How can multimodal learning analytics infrastructure be designed to support real-time data acquisition and processing effectively?; (RQ2) how to use smartwatch sensor data to infer environmental context and physical activities to complement learning analytics indicators for blended learning sessions; and (RQ3) how can we align the extracted multimodal indicators with pedagogical interventions. RQ1 was investigated by a structured literature review and by conducting eleven semi-structured interviews with LA infrastructure developers. According to RQ2, we are currently designing and implementing a multimodal learning analytics infrastructure to collect and process sensor and experience data from Smartwatches. Finally, according to RQ3, an exploratory field study will be conducted to extract multimodal learning indicators and examine them with learners and pedagogical experts to develop effective interventions. Researchers, educators, and learners can use and adapt our contributions to gain new insights into learners' time and learning tactics, and physical learning spaces from learning sessions taking place in-the-wild.
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