In this paper, we explore the use of computer vision techniques to analyze students' moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student's mood and affective state. We find that the problem of accurately determining a child's mood from a single video frame is surprisingly difficult, even for humans. However when the system is allowed to make decisions based on information from 10 to 30 seconds of video, excellent performance may be obtained.
JF - 2011 IEEE International Conference on Automatic Face Gesture Recognition and Workshops (FG 2011) PB - IEEE CY - Santa Barbara, CA SN - 978-1-4244-9140-7 ER - TY - CONF T1 - Automatic cry detection in early childhood education settings T2 - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 Y1 - 2008 A1 - Ruvolo, P. A1 - Movellan, J. KW - Acoustic noise KW - auditory moods KW - automatic cry detection KW - behavioural sciences computing KW - Deafness KW - early childhood education settings KW - education KW - Educational robots KW - Emotion recognition KW - human coders KW - Humans KW - learning (artificial intelligence) KW - Machine Learning KW - Mood KW - preschool classrooms KW - Prototypes KW - Robustness KW - Working environment noise AB -We present results on applying a novel machine learning approach for learning auditory moods in natural environments [1] to the problem of detecting crying episodes in preschool classrooms. The resulting system achieved levels of performance approaching that of human coders and also significantly outperformed previous approaches to this problem [2].
JF - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 PB - IEEE CY - Monterey, CA SN - 978-1-4244-2661-4 ER - TY - CONF T1 - Building a more effective teaching robot using apprenticeship learning T2 - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 Y1 - 2008 A1 - Ruvolo, P. A1 - Whitehill, J. A1 - Virnes, M. A1 - Movellan, J. KW - apprenticeship learning KW - automated helicopter flight KW - Automatic control KW - Data mining KW - Delay KW - education KW - Educational robots KW - expert teaching KW - Helicopters KW - Human-robot interaction KW - humanoid robots KW - Humans Learning systems KW - mechanical control KW - robot teaching KW - Robotics and Automation KW - RUBI social robot KW - time 18 month to 24 month KW - timing AB -What defines good teaching? While attributes such as timing, responsiveness to social cues, and pacing of material clearly play a role, it is difficult to create a comprehensive specification of what it means to be a good teacher. On the other hand, it is relatively easy to obtain examples of expert teaching behavior by observing a real teacher. With this inspiration as our guide, we investigated apprenticeship learning methods [1] that use data recorded from expert teachers as a means of improving the teaching abilities of RUBI, a social robot immersed in a classroom of 18-24 month old children. While this approach has achieved considerable success in mechanical control, such as automated helicopter flight [2], until now there has been little work on applying it to the field of social robotics. This paper explores two particular approaches to apprenticeship learning, and analyzes the models of teaching that each approach learns from the data of the human teacher. Empirical results indicate that the apprenticeship learning paradigm, though still nascent in its use in the social robotics field, holds promise, and that our proposed methods can already extract meaningful teaching models from demonstrations of a human expert.
JF - 7th IEEE International Conference on Development and Learning, 2008. ICDL 2008 PB - IEE CY - Monterey, CA SN - 978-1-4244-2661-4 ER -