Multimodal Data Analytics for Assessing Collaborative Interactions
Author ORCID Identifier
Yanghee Kim:https://orcid.org/0000-0002-0611-6254
Sachit Butail:https://orcid.org/0000-0001-9785-7375
Michael Tscholl:https://orcid.org/0000-0001-5528-4635
Jaejin Hwang:https://orcid.org/0000-0001-5831-9688
Publication Title
Computer-Supported Collaborative Learning Conference, CSCL
ISSN
15734552
E-ISSN
43983
ISBN
9781732467293
Document Type
Conference Proceeding
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.
First Page
2547
Last Page
2554
Publication Date
1-1-2020
Recommended Citation
Kim, Yanghee; Butail, Sachit; Liu, Lichual; Tscholl, Michael; Hwang, Jaejin; Cafaro, Francesco; Trajkova, Milka; Kwon, Kyungbin; Espino, Danielle; Lee, Seung; Hamilton, Eric; D'Angelo, Cynthia; Ochoa, Xavier; Kline, Aaron; and Lee, Sungchul, "Multimodal Data Analytics for Assessing Collaborative Interactions" (2020). NIU Bibliography. 344.
https://huskiecommons.lib.niu.edu/niubib/344
Department
Department of Educational Technology, Research and Assessment (ETRA); Department of Mechanical Engineering; Department of Electrical Engineering; Other; Department of Industrial and Systems Engineering