Estimating the class participation of children using face information for reflection

Abstract

Currently, "quality of education" is a key issue in the field of education. To improve this, it is effective to reflect on lessons. However, in actual educational settings, it is difficult for teachers to record every child in class. To address this issue, we believe that the amount of events occurring to each child can be estimated from the amount of behavior, and we are working to extract the amount of behavior using AI technology from the data measured by our class situation sensing system. Based on this, we have examined the possibility of estimating class participation attitudes using behavior quantities by comparing the extracted behavior quantities of individual children with manually assessed class participation attitudes. In this presentation, we report on the current state of the art regarding the estimation of class participation and discuss how it can be used in teacher reflection situations.