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Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2015 Vol. 5(2): 148-152 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2015.V5.499

Automatic Engagement Level Estimation of Kids in a Learning Environment

Woo-Han Yun, Dongjin Lee, Chankyu Park, and Jaehong Kim
Abstract—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.

Index Terms—Engagement level estimation, facial expression, facial feature, kid, multiple intelligence.

Woo-han Yun, Dongjin Lee, Chankyu Park, and Jaehong Kim are with Human-Robot Interaction Section, Electronics and Telecommunications Research Institute, Daejeon, South Korea (e-mail: yochin@etri.re.kr, robin2002@etri.re.kr, parkck@etri.re.kr, jhkim504@etri.re.kr).

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Cite: Woo-Han Yun, Dongjin Lee, Chankyu Park, and Jaehong Kim, "Automatic Engagement Level Estimation of Kids in a Learning Environment," International Journal of Machine Learning and Computing vol. 5, no. 2, pp. 148-152, 2015.

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