Abstract
This paper presents a time-series multidimensional dialogue feature visualization method for group work. The new coronavirus has changed our lives and brought many things online. Group work is more prevalent now than ever before, as online access has eliminated location restrictions in all situations, allowing multiple people to gather and share ideas. However, when group work is conducted, discussions and opinions may not proceed smoothly, and sometimes group work is meaningless. This method uses group work recording data and Live Transcription data as input and performs time-series multidimensional dialogue feature visualization to show group work visualization results as output. The results of the visualization are shown using data from group work, and it is considered possible to visualize information on whether the discussion is active or not at a certain time and whether the discussion is organized or not.
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Ohnishi, R. et al. (2023). Time-Series Multidimensional Dialogue Feature Visualization Method for Group Work. In: Lee, R. (eds) Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2022-Winter. Studies in Computational Intelligence, vol 1086. Springer, Cham. https://doi.org/10.1007/978-3-031-26135-0_6
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