Optimizing Learning in Robot-Child Tutoring through Personalized Timing Strategies

Abstract

This research focuses on exploring personalized timing strategies to optimize learning in robot-child tutoring. Non-task breaks are commonly used in education to address children's limited attention spans and promote cognitive rejuvenation. Robots provide a valuable opportunity to deliver personalized breaks tailored to the specific needs of individual students, enhancing their learning experiences. We develop an autonomous robot tutoring system that assesses students' performance and administers breaks based on personalized schedules aligned with their individual progress. Through a field study, we compare the effectiveness of different break strategies, including a fixed timing approach, a reward strategy that aligns break timing with performance improvements, and a refocus strategy that aligns break timing with performance declines. Our results demonstrate that personalized strategies significantly enhance children's learning outcomes compared to the fixed strategy. Furthermore, we observe immediate benefits in terms of improved efficiency and accuracy in completing educational tasks following personalized breaks, underscoring the restorative effects of breaks when provided at optimal moments. These findings contribute to the understanding of how personalized timing strategies can optimize learning in the context of robot-child tutoring.