A Web Application with Business Card-Type Sensors for Collaborative Learning Analysis

Yamaguchi, Daichi; Yamaguchi, Shunpei; Oshima, Ritsuko; Oshima, Jun; Fujihashi, Takuya; Saruwatari, Shunsuke; Watanabe, Takashi

doi:10.1109/gcce53005.2021.9621819

Cited by 1 publication

(7 citation statements)

References 6 publications

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Section: A Collaboration Analysis With Iot Badgesmentioning

confidence: 99%

“…To solve these drawbacks, an earlier and briefer version of this article was presented in the study of [11]. The study in [11] proposed a Django-based web application with SRP Badges, named the Sensor-based Regulation Profiler Web (SRP Web), to support collaborative learning analysis. This paper proposes an extended version of the SRP Web, named SRP Web Services, as user-friendly web services for qualitative analysis of collaborative learning.…”

Section: A Collaboration Analysis With Iot Badgesmentioning

confidence: 99%

“…Some studies have developed user-friendly web services for sensor data analysis [11], [18]- [29]. For example, the study in [23] presented a model for smart agriculture to monitor real-time soil properties and to remotely control various operations of the field anytime by mobile and web application.…”

Section: B Web Services For Sensor Data Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…To overcome the above problems, an earlier and briefer version of this article was presented in the study of [11]. The study in [11] proposed a web application with IoT badges [2] named the Sensor-based Regulation Profiler Web (SRP Web) to support qualitative analysis for collaborative learning. The proposed scheme consists of an SRP Badge and a web application for learning analysis that extracts and visualizes collaborative learning activity from the acquired sensor data.…”

Section: Introductionmentioning

confidence: 99%

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Web Services for Collaboration Analysis With IoT Badges

et al. 2022

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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 [1]-[4] 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 userfriendly 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.

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Section: A Collaboration Analysis With Iot Badgesmentioning

confidence: 99%

Section: A Collaboration Analysis With Iot Badgesmentioning

confidence: 99%