An exploratory approach to measuring collaborative engagement in child robot interaction

Abstract

This study explored data analytic approaches to assessing young children's engagement in robot-mediated collaborative interaction. To develop our analytic models, we took a case-study approach and looked closely into four children's behaviors during three conversational sessions. Grounded in engagement theory, three sources of multimodal behavioral data (utterances, kinesics, and vocie) were coded through human annotation and automatic speech recognition and analysis. Then, information-theoretic methods were used to uncover nonlinear dependencies (called mutual information) among the multimodal behaviors of each child. From this, we derived a model to compute a compound variable of engagement. This computation produced engagement trends of each child, the engagement relationship between two children in a pair, and the engagement relationship with the robot over time. The computed trends corresponded well with the data from human observations. This approach has implications for quantifying engagement from rich and natural multimodal behaviors.