Give Me a Break! Personalized Timing Strategies to Promote Learning in Robot-Child Tutoring

A common practice in education to accommodate the short attention spans of children during learning is to provide them with non-task breaks for cognitive rest. Holding great promise to promote learning, robots can provide these breaks at times personalized to individual children. In this work, we investigate personalized timing strategies for providing breaks to young learners during a robot tutoring interaction. We build an autonomous robot tutoring system that monitors student performance and provides break activities based on a personalized schedule according to performance. We conduct a field study to explore the effects of different strategies for providing breaks during tutoring. By comparing a fixed timing strategy with a reward strategy (break timing personalized to performance gains) and a refocus strategy (break timing personalized to performance drops), we show that the personalized strategies promote learning gains for children more effectively than the fixed strategy. Our results also reveal immediate benefits in enhancing efficiency and accuracy in completing educational problems after personalized breaks, showing the restorative effects of the breaks when administered at the right time.