Tailored Timing Approaches for Enhancing Learning in Robot-Child Tutoring
Aditi Rao, Anshul Mishra, Andreas A. J. Wismeijer · 2023
In the realm of education, it is common practice to incorporate non-task breaks to cater to children's limited attention spans and provide cognitive rest. With the potential to support learning, robots can play a crucial role in offering personalized breaks tailored to individual children. This research focuses on investigating personalized timing strategies for delivering breaks to young learners during robot tutoring interactions. We develop an independent robot tutoring system that monitors students' performance and administers break activities based on personalized schedules aligned with their performance levels. To explore the effects of various break strategies during tutoring, we conduct a field study. By comparing a fixed timing strategy with two personalized approaches - a reward strategy (break timing personalized to performance improvements) and a refocus strategy (break timing personalized to performance declines) - we demonstrate that the personalized strategies effectively enhance learning outcomes for children compared to the fixed strategy. Our findings also reveal immediate advantages in terms of increased efficiency and accuracy in completing educational tasks following personalized breaks, highlighting the restorative effects of breaks when provided at the appropriate moments.