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Title: Multimodal Data Analytics for Assessing Collaborative Interactions
Authors: Kim, Yanghee
D'Angelo, Cynthia
Cafaro, Francesco
Ochoa, Xavier
Espino, Danielle
Kline, Aaron
Hamilton, Eric
Lee, Seung
Butail, Sachit
Liu, Lichuan
Trajkova, Milka
Tscholl, Michael
Hwang, Jaejin
Lee, Sungchul
Kwon, Kyungbin
Keywords: Scale
Issue Date: Jun-2020
Publisher: International Society of the Learning Sciences (ISLS)
Citation: Kim, Y., D'Angelo, C., Cafaro, F., Ochoa, X., Espino, D., Kline, A., Hamilton, E., Lee, S., Butail, S., Liu, L., Trajkova, M., Tscholl, M., Hwang, J., Lee, S., & Kwon, K. (2020). Multimodal Data Analytics for Assessing Collaborative Interactions. In Gresalfi, M. and Horn, I. S. (Eds.), The Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020, Volume 5 (pp. 2547-2554). Nashville, Tennessee: International Society of the Learning Sciences.
Abstract: This symposium will discuss the current status of the research and development of multimodal data analytics (MDA) for the observation of collaboration. Five research groups will present their current work on MDA, each with a unique focus on different data sources and different approaches to the analysis and synthesis of multimodal data sets. A few themes emerge from these studies: i) the studies seek to examine collaborative behaviors as a process in ordinary settings, both formal and informal; ii) with MDA being in its early stage, manual and computational approaches are taken complementarily, also using human annotation as the ground truth for the computational approach; and iii) several different discipline-specific research and development lines contribute integrally to generating authentic measures of collaborative interactions in situ, making this line of research transdisciplinary.
Appears in Collections:ICLS 2020

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