Multimodal Data Analytics for Assessing Collaborative Interactions

dc.contributor.authorKim, Yanghee
dc.contributor.authorD'Angelo, Cynthia
dc.contributor.authorCafaro, Francesco
dc.contributor.authorOchoa, Xavier
dc.contributor.authorEspino, Danielle
dc.contributor.authorKline, Aaron
dc.contributor.authorHamilton, Eric
dc.contributor.authorLee, Seung
dc.contributor.authorButail, Sachit
dc.contributor.authorLiu, Lichuan
dc.contributor.authorTrajkova, Milka
dc.contributor.authorTscholl, Michael
dc.contributor.authorHwang, Jaejin
dc.contributor.authorLee, Sungchul
dc.contributor.authorKwon, Kyungbin
dc.contributor.departmentHuman-Centered Computing, School of Informatics and Computingen_US
dc.date.accessioned2021-10-27T17:40:53Z
dc.date.available2021-10-27T17:40:53Z
dc.date.issued2020-06
dc.description.abstractThis 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.en_US
dc.eprint.versionFinal published versionen_US
dc.identifier.citationKim, 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.en_US
dc.identifier.urihttps://hdl.handle.net/1805/26889
dc.language.isoenen_US
dc.publisherInternational Society of the Learning Sciences (ISLS)en_US
dc.relation.isversionof10.22318/icls2020.2547en_US
dc.relation.journalThe Interdisciplinarity of the Learning Sciences, 14th International Conference of the Learning Sciences (ICLS) 2020en_US
dc.rightsPublisher Policyen_US
dc.sourcePublisheren_US
dc.subjectmultimodal data analysisen_US
dc.subjecthuman annotationen_US
dc.subjectcomputational approachesen_US
dc.titleMultimodal Data Analytics for Assessing Collaborative Interactionsen_US
dc.typeArticleen_US
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