Enhancing Learning in Robot-Child Tutoring with Personalized Timing Strategies

Abstract

This study focuses on optimizing learning in robot-child tutoring by implementing personalized timing strategies. Non-task breaks are commonly used in education to address children's limited attention spans and promote cognitive rejuvenation. Robots offer a unique opportunity to deliver tailored breaks that meet the individual needs of students, thereby enhancing their learning experiences. In this research, we develop an autonomous robot tutoring system that monitors students' performance and administers breaks based on personalized schedules aligned with their progress. Through a field study, we compare the effectiveness of different timing 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. The results demonstrate that personalized timing strategies significantly optimize learning outcomes in robot-child tutoring compared to the fixed timing strategy. Additionally, we observe immediate benefits, such as increased efficiency and accuracy in completing educational tasks, following personalized breaks. This study highlights the importance of personalized timing in promoting effective learning and offers insights into improving robot-child tutoring experiences.