Abstract
Although most studies have been focused on the language data for investigating cultural practices of learning in the quantitative ethnography, human communication is fundamentally multimodal. The integration of non-language modality data with language data would provide more accurate interpretation of how learners engage in their collaboration. In this study, therefore, we propose Sensor-based Regulation Profiler. Sensor-based Regulation Profiler automatically extracts and visualizes the points that researchers in learning science should notice to support qualitative analysis in collaborative learning. The Sensor-based Regulation Profiler consists of a business card-type sensor that acquires sensor data from each learner as well as a data mining technique that analyzes the acquired sensor data. The proposed data mining technique automatically extracts and visualizes social graphs, learning phases, and speakers during collaborative learning to reduce the costs of qualitative analysis. Experimental evaluations using business card sensors in collaborative learning showed that the social graph, and the learning phase could be automatically extracted and visualized from the acquired sensor data and the speaker identification could be realized with an average accuracy of 77.8% by using the Sensor-based Regulation Profiler.
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This work was supported by JSPS KAKENHI (JP16H01718, 18H03231, 19H01101).
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Yamaguchi, S. et al. (2021). Collaborative Learning Analysis Using Business Card-Type Sensors. In: Ruis, A.R., Lee, S.B. (eds) Advances in Quantitative Ethnography. ICQE 2021. Communications in Computer and Information Science, vol 1312. Springer, Cham. https://doi.org/10.1007/978-3-030-67788-6_22
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