| Autor: |
Shunpei Yamaguchi, Motoki Nagano, Shunpei Ohira, Ritsuko Oshima, Jun Oshima, Takuya Fujihashi, Shunsuke Saruwatari, Takashi Watanabe |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Access, Vol 10, Pp 121318-121328 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
2169-3536 |
| DOI: |
10.1109/ACCESS.2022.3222562 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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