Body Posture Analysis for the Classification of Classroom Scenes

Abstract

Student feedback is useful for teachers to improve their teaching. Although it is common to receive student ratings in universities, the low frequency of such feedback reduces the utility of the information. Using methods that do not rely on ratings can increase the frequency of feedback. We investigated whether the body posture of students can be used as an indicator of classroom engagement. In this paper, we estimated body posture from videos taken of students in the audience during a presentation and classified the scenes based on the postural similarity. The obtained clusters showed that body posture changed over time and did not return to the original state. A comparison between clusters at the beginning and end of the presentation showed that the standard deviation of head direction becomes large at the end, suggesting that body posture might reflect the degree of distraction. We discussed how body posture information facilitates teachers' reflection.