Abstract
Active learning has gained attention in recent years and has been implemented across various education fields, as it is considered more effective than the conventional lecture style of learning. In active learning, it is necessary for teachers to provide appropriate facilitation to improve the quality of learning. However, it becomes more difficult to keep track of the activities of each group as the number of groups increases. Therefore, in this study, we analyzed body movements based on the values obtained from the accelerometer and gyroscope of a smartphone worn around the neck or placed in pant pockets to evaluate the learning state of students. Furthermore, we proposed appropriate facilitation based on the evaluated students’ learning states according to the analyzed body movements and constructed a prototype system enabling a robot to perform facilitation.
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Suzuki, S., Yamamoto, T. (2023). Prototype of a Facilitation System for Active Learning Using Deep Learning in Body Movement Classification. In: Kurosu, M., Hashizume, A. (eds) Human-Computer Interaction. HCII 2023. Lecture Notes in Computer Science, vol 14014. Springer, Cham. https://doi.org/10.1007/978-3-031-35572-1_24
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DOI: https://doi.org/10.1007/978-3-031-35572-1_24
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