Artificial Intelligence in Education

Recent years have seen a growing recognition of the central role of affect and motivation in learning. In particular, nonverbal behaviors such as posture and gesture provide key channels signaling affective and motivational states. Developing a clear understanding of these mechanisms will inform the development of personalized learning environments that promote successful affective and motivational outcomes. This paper investigates posture and gesture in computer-mediated tutorial dialogue using automated techniques to track posture and hand-to-face gestures. Annotated dialogue transcripts were analyzed to identify the relationships between student posture, student gesture, and tutor and student dialogue. The results indicate that posture and hand-to-face gestures are significantly associated with particular tutorial dialogue moves. Additionally, two-hands-to-face gestures occurred significantly more frequently among students with low self-efficacy. The results shed light on the cognitiveaffective mechanisms that underlie these nonverbal behaviors. Collectively, the findings provide insight into the interdependencies among tutorial dialogue, posture, and gesture, revealing a new avenue for automated tracking of embodied affect during learning.