Author:
Pareto Lena,Ekström Sara,Serholt Sofia
Abstract
Human peer tutoring is known to be effective for learning, and social robots are currently being explored for robot-assisted peer tutoring. In peer tutoring, not only the tutee but also the tutor benefit from the activity. Exploiting the learning-by-teaching mechanism, robots as tutees can be a promising approach for tutor learning. This study compares robots and humans by examining children’s learning-by-teaching with a social robot and younger children, respectively. The study comprised a small-scale field experiment in a Swedish primary school, following a within-subject design. Ten sixth-grade students (age 12–13) assigned as tutors conducted two 30 min peer tutoring sessions each, one with a robot tutee and one with a third-grade student (age 9–10) as the tutee. The tutoring task consisted of teaching the tutee to play a two-player educational game designed to promote conceptual understanding and mathematical thinking. The tutoring sessions were video recorded, and verbal actions were transcribed and extended with crucial game actions and user gestures, to explore differences in interaction patterns between the two conditions. An extension to the classical initiation–response–feedback framework for classroom interactions, the IRFCE tutoring framework, was modified and used as an analytic lens. Actors, tutoring actions, and teaching interactions were examined and coded as they unfolded in the respective child–robot and child–child interactions during the sessions. Significant differences between the robot tutee and child tutee conditions regarding action frequencies and characteristics were found, concerning tutee initiatives, tutee questions, tutor explanations, tutee involvement, and evaluation feedback. We have identified ample opportunities for the tutor to learn from teaching in both conditions, for different reasons. The child tutee condition provided opportunities to engage in explanations to the tutee, experience smooth collaboration, and gain motivation through social responsibility for the younger child. The robot tutee condition provided opportunities to answer challenging questions from the tutee, receive plenty of feedback, and communicate using mathematical language. Hence, both conditions provide good learning opportunities for a tutor, but in different ways.
Funder
Marcus och Amalia Wallenbergs minnesfond
Vetenskapsrådet
Subject
Artificial Intelligence,Computer Science Applications