Web Services for Collaboration Analysis With IoT Badges
Web Services for Collaboration Analysis With IoT Badges
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DOI：
10.1109/access.2022.3222562
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发表时间：
2022
期刊：
影响因子：
3.9
通讯作者：
Shunpei Yamaguchi;Motoki Nagano;Shunpei Ohira;Ritsuko Oshima;J. Oshima;T. Fujihashi;S. Saruwatari;Takashi Watanabe
中科院分区：
文献类型：
--
作者：
Shunpei Yamaguchi;Motoki Nagano;Shunpei Ohira;Ritsuko Oshima;J. Oshima;T. Fujihashi;S. Saruwatari;Takashi Watanabe
协作学习是一种教学方法，涉及学习者群体合作解决问题，完成任务或创建产品。为了提高协作学习的性能，Yamaguchi等人（2021，2021，2021和2022）的研究开发了物联网系统，并定量提取了学习者之间的协作。这些研究从学习者的物联网徽章中获取传感器数据，并在计算机上使用获取的传感器数据分析学习活动。然而，由于软件安装和命令行界面操作复杂，现有研究对不熟悉信息技术的学习分析员来说并不方便。这些弊端阻碍了技术的广泛推广和学习科学新模式的探索。考虑到分析人员的高可用性，本文提出了一种新的Web服务，称为基于传感器的监管分析器Web服务（SRP Web服务），用于与物联网徽章的协作分析。建议的Web应用程序由Next.js前端和FastAPI，SQLite和Python后端组成，并从Web浏览器上获取的传感器数据中提取分析师学习活动中的关键点。实验评估表明，建议的Web服务支持学习分析师在定量分析的学习活动具有较高的可用性。此外，SRP Web服务可扩展到数百个用户。
Collaborative learning is an educational approach to teaching and learning that involves groups of learners collaborating to solve a problem, complete a task, or create a product. To enhance the performance of collaborative learning, the studies in Yamaguchi et al. (2021, 2021, 2021, and 2022) develop an IoT system and quantitatively extract collaboration between learners. The studies acquire sensor data from IoT badges on learners and analyze learning activities with the acquired sensor data on a computer. However, existing studies are not user-friendly for learning analysts who are unfamiliar with information technology owing to complex software installation and command line interface (CLI) operation. Such drawbacks hinder the wide expansion of technology and the exploration of new learning patterns in learning science. Considering high usability for analysts, this paper proposes novel web services named Sensor-based Regulation Profiler Web Services (SRP Web Services) for collaboration analysis with IoT badges. The proposed web application consists of front-end on Next.js and back-end on FastAPI, SQLite, and Python and extracts key points in learning activities for the analysts from the acquired sensor data on a web browser. Experimental evaluations showed that the proposed web services support learning analysts in quantitative analysis of learning activities with high usability. In addition, SRP Web Services are scalable with hundreds of users.