Personalized Timing Strategies to Optimize Learning in Robot-Child Tutoring
shalini Singh, Aditya Subramaniam, Ramesh Bhatta, Pankaj Dixit · 2023
In the field of education, it is customary to incorporate non-task breaks to accommodate children's limited attention spans and promote cognitive rejuvenation. Robots have emerged as a valuable resource for delivering customized breaks tailored to the unique needs of individual students, thereby supporting their learning experiences. This study delves into the investigation of personalized timing strategies for providing breaks to young learners during robot tutoring sessions. We develop an autonomous robot tutoring system that assesses students' performance and administers breaks based on personalized schedules that align with their individual progress. To analyze the impact of different break strategies on tutoring outcomes, we conduct a field study. By comparing a fixed timing strategy with two personalized approaches - a reward strategy (aligning break timing with performance improvements) and a refocus strategy (aligning break timing with performance declines) - we substantiate that the personalized strategies effectively enhance children's learning outcomes in comparison to the fixed strategy. Additionally, our findings highlight immediate benefits, such as improved efficiency and accuracy in completing educational tasks, following personalized breaks. These results underscore the restorative effects of breaks when administered at optimal moments.