Identifying principal social signals in private student-teacher interactions for robot-enhanced education

Abstract

Providing robots with social intelligence is critical for making entertaining and sustainable human-robot interactions. The first step to get good social intelligence is to appropriately understand the meaning of social signals emitted by interactors. In this paper, we introduce a preliminary study on identifying principal social signals in interpreting participant's engagement and confirmation intention in 1:1 interactions. We annotated 6 video recordings of private teacher-student interactions with 20 social signals and their interpretations, and built pattern data sets with different subsets of social signals. C4.5 based decision trees were generated using the pattern data sets and the recall rates were compared. Also attribute selection was performed to find principal social signals. The results showed that verbal signal was the most principal for determining engagement, and the combination of gaze and verbal signal for confirmation intention.