In this paper, we explore the use of computer vision techniques to analyze students{\textquoteright} moods during one-on-one teaching interactions. The eventual goal is to create automated tutoring systems that are sensitive to the student{\textquoteright}s mood and affective state. We find that the problem of accurately determining a child{\textquoteright}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.
}, keywords = {automated facial affect analysis, automated tutoring system, behavioural sciences computing, computer vision technique, Context, decision making, education, Emotion recognition, face recognition, Human, human computer interaction, Labeling, Machine Learning, Mood, n Histograms, one-on-one tutoring application, s Intelligent tutoring systems, student mood analysis}, isbn = {978-1-4244-9140-7}, author = {Butko, N. and Theocharous, G. and Philipose, M. and Movellan, J.} } @conference {60, title = {Automatic cry detection in early childhood education settings}, booktitle = {7th IEEE International Conference on Development and Learning, 2008. ICDL 2008}, year = {2008}, month = {08/2008}, publisher = {IEEE}, organization = {IEEE}, address = {Monterey, CA}, abstract = {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].
}, keywords = {Acoustic noise, auditory moods, automatic cry detection, behavioural sciences computing, Deafness, early childhood education settings, education, Educational robots, Emotion recognition, human coders, Humans, learning (artificial intelligence), Machine Learning, Mood, preschool classrooms, Prototypes, Robustness, Working environment noise}, isbn = {978-1-4244-2661-4}, author = {Ruvolo, P. and Movellan, J.} } @conference {59, title = {Building a more effective teaching robot using apprenticeship learning}, booktitle = {7th IEEE International Conference on Development and Learning, 2008. ICDL 2008}, year = {2008}, month = {08/2008}, publisher = {IEE}, organization = {IEE}, address = {Monterey, CA}, abstract = {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.
}, keywords = {apprenticeship learning, automated helicopter flight, Automatic control, Data mining, Delay, education, Educational robots, expert teaching, Helicopters, Human-robot interaction, humanoid robots, Humans Learning systems, mechanical control, robot teaching, Robotics and Automation, RUBI social robot, time 18 month to 24 month, timing}, isbn = {978-1-4244-2661-4}, author = {Ruvolo, P. and Whitehill, J. and Virnes, M. and Movellan, J.} }