Multimodal Data Analytics for Assessing Collaborative Interactions
Yanghee Anna Kim, Cynthia M. D’Angelo, Francesco Cafaro, Xavier Ochôa, Danielle P. Espino, Aaron Kline, Eric R. Hamilton, Seung B. Lee, Sachit Butail, Lichuan Liu, Milka Trajkova, Michael Tscholl, Jaejin Hwang, Sungchul Lee, Kyungbin Kwon · IUScholarWorks (Indiana University) · 2020
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.