Automatic Engagement Level Estimation of Kids in a Learning Environment
Woo‐han Yun, Dongjin Lee, Chankyu Park, Jaehong Kim · International Journal of Machine Learning and Computing · 2015
This study is about the automatic engagement level measuring system which extracts features of kids taking tests with a desktop computer and estimates an engagement level.We recorded 12 kids from two difference kindergartens for 5 days.The test consists of 6 subjects with 2 sessions (levels).The recorded RGB video data is divided into 30 second video clips which are labeled by an expert.Cues reflecting face and head information are extracted from video data.The cues are aggregated for 30 seconds and used for estimating the engagement level.We used a relevance vector classifier to estimate an engagement level.We also analyze the data using linear regression analysis and find valid features.The system shows a promising performance of engagement level estimation of kids.