A keynote speaker at the ICDM 2019 TMDM Workshop (Translational Multimedia Data Mining for AI-Based Medical Diagnostics—Bridging Digital Intelligence with Clinical Practices), Dr. Daniel Messinger also presented two papers on Friday, November 8, 2020.
The second paper presented was “Audio-Based Group Detection for Classroom Dynamics Analysis.” It was published in IEEE Xplore on January 13, 2020.
Detecting conversational groups has recently drawn much attention since it plays an important role in social group analysis, social robotics, and video surveillance. Such results can be applied to a variety of application domains, such as improving human-robot interaction and automated comprehension of social communications. Moreover, the automated group detection results can provide objective and quantitative measurements for interactive human behavior analysis. This particular analysis can be an initial step in understanding the classroom dynamics and the effects of social interactions on children’s cognitive and social development.
Young children’s interactions, both with teachers and peers, in early education programs have a long-lasting impact on their cognitive and social development. The exposure to the variegated and sophisticated vocabulary from teachers is related to the preschoolers’ language gains, including the growth in syntactic comprehension and oral language skills, as well as their later reading comprehension abilities. Read more . . .
Y. Tao, S. G. Mitsven, L. K. Perry, D. S. Messinger and M. Shyu, Audio-Based Group Detection for Classroom Dynamics Analysis, 2019 International Conference on Data Mining Workshops (ICDMW), Beijing, China, 2019, pp. 855-862, doi: 10.1109/ICDMW.2019.00125.