“I Know That Now, I’m Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor
Research output: Contribution to journal › Article › peer-review
Colleges, School and Institutes
Robots are increasingly being used to provide motivating, engaging and personalised support to learners. 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. It presents a study where a robotic tutor supports reflection and SRL processes with an OLM. An OLM allows the learner to view the model that the system holds about them. In this study, participants take part in a geography-based task on a touch screen with different levels of adaptive feedback provided by the robot. 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 and increased learning gain can be observed over control conditions where the robotic tutor does not provide SRL scaffolding. We also find that pressure and tension in the activity increases and perception of the robot is less favourable in conditions where the robotic tutor makes the learner aware that there are issues but does not provide specific help to address these issues.
|Journal||International Journal of Social Robotics|
|Early online date||23 Nov 2017|
|Publication status||E-pub ahead of print - 23 Nov 2017|
- robotic tutors , personalisation , self-regulated learning , child-robot interaction