Bin Chen
ABSTRACT
In order to orchestrate collaborative group work, such as a brainstorming workshop or active learning class, a facilitator must monitor all groups simultaneously. In this paper, we propose a method for recognizing group activities, based on machine learning, where the input features are derived from the analysis of raw system events generated by a collaboration system known as "creative digital space" that we developed before. To verify the effectiveness of the proposed method, we also evaluated the results obtained by using the test data collected from active learning classes.
- Roberto Martínez Maldonado, Kalina Yacef, and Judy Kay. 2015. TSCL: A conceptual model to inform understanding of collaborative learning processes at interactive tabletops. Int. J. Hum.-Comput. Stud. , Vol. 83 (2015), 62--82.Google Scholar
Digital Library
- Roberto Martinez, James R. Wallace, Judy Kay, and Kalina Yacef. 2011. Modelling and Identifying Collaborative Situations in a Collocated Multi-display Groupware Setting. In Proceedings of the 15th International Conference on Artificial Intelligence in Education (AIED'11). Springer-Verlag, 196--204.Google ScholarDigital Library
- Keiju Okabayashi, Masashi Uyama, Junichi Yura, and Riichiro Take. 2018. Creative digital spaces technology encourage inspired human communication. FUJITSU Sci. Tech. J. , Vol. 54, 1, 33--39.Google Scholar
- Kurt VanLehn. 2017. High Accuracy Detection of Collaboration From Log Data and Superficial Speech Features. In CSCL .Google Scholar
Index Terms
- Group Activity Recognition to Support Collaboration in Creative Digital Space
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