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Collaborative learning practices foster the ability to solve creative problems in collaboration with other learners. The collaboration enables learners to learn new ideas from other learners and enhances the social ability of the learners through interaction with other learners. Although the learning science field now uses qualitative analysis to analyze the effects of the collaborative discourse, qualitative analysis requires much human and time costs to analyze the collaborative discourse with dozens of students. This study proposes Sensor-based Regulation Profiler to reduce the analysis costs. The proposed scheme consists of the business card-type sensors that acquire sensor data from each learner with a precise time synchronization as well as learning analysis methods that analyze the collaborative discourse from the acquired sensor data. Experimental evaluations using the proposed scheme showed that the proposed business card-type sensors realized a time synchronization error of 7.7 μs on average across the sensors. In addition, the proposed learning analysis could extract and visualize the collaborative activity of each learner in the collaborative discourse through the social graph extraction, learning phase extraction, speaker identification, and activity estimation by using the sensor data from the proposed business card-type sensors.

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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.