Adaptive Robotic Tutors that Support Self-Regulated Learning: A Longer-Term Investigation with Primary School Children

Abstract

Robots are increasingly being used to provide motivating, engaging and personalised support to learners. These robotic tutors have been able to increase student learning gain by providing personalised hints or problem selection. However, they have never been used to assist children in developing self regulated learning (SRL) skills. SRL skills allow a learner to more effectively self-assess and guide their own learning; learners that engage these skills have been shown to perform better academically. This paper explores how personalised tutoring by a robot achieved using an open learner model (OLM) promotes SRL processes and how this can impact learning and SRL skills compared to personalised domain support alone. An OLM allows the learner to view the model that the system holds about them. We present a longer-term study where participants take part in a geography-based task on a touch screen with adaptive feedback provided by the robot. In addition to domain support the robotic tutor uses an OLM to prompt the learner to monitor their developing skills, set goals, and use appropriate tools. Results show that, when a robotic tutor personalises and adaptively scaffolds SRL behaviour based upon an OLM, greater indication of SRL behaviour can be observed over the control condition where the robotic tutor only provides domain support and not SRL scaffolding.