“I Know That Now, I’m Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor

International Journal of Social Robotics, Nov 2017

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.

A PDF file should load here. If you do not see its contents the file may be temporarily unavailable at the journal website or you do not have a PDF plug-in installed and enabled in your browser.

Alternatively, you can download the file locally and open with any standalone PDF reader:

https://link.springer.com/content/pdf/10.1007%2Fs12369-017-0430-y.pdf

“I Know That Now, I’m Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor

“I Know That Now, I'm Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor Aidan Jones 0 1 Susan Bull 0 1 Ginevra Castellano 0 1 0 Department of Information Technology, Uppsala University , Uppsala , Sweden 1 Department of Electronic, Electrical and Systems Engineering, School of Engineering, University of Birmingham , Birmingham B15 2TT , UK 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 Robotic tutors; Personalisation; Self-regulated learning; Child-robot interaction - 2 Institute of Education, University College London, London, UK 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. 1 Introduction Robots are increasingly being used to provide motivating, engaging and personalised support to learners [ 26 ]. Robotic tutors have been able to increase problem solving time by providing personalised hints [ 21 ] or increase learning gain by personalised problem selection [ 9 ]. Yet, they have never been used to assist children in developing self-regulated learning (SRL) skills. SRL is the meta-cognitive process where a student uses self-assessment, goal setting, and the selecting and deploying of strategies to acquire academic skills [ 44 ]. The use of SRL strategies are significantly correlated with measures of academic performance [ 44 ]. By supporting these skills students may be able to learn more effectively, even outside of the tutoring session. 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 in primary school children (Fig. 1). We describe a study where a robotic tutor provides different levels of personalised SRL scaffolding to primary school children. The autonomous robotic tutor’s behaviour builds upon information provided to a student in an OLM. OLM externalise the model that the system has of the learner in a way that is interpretable by the learner [ 5 ]. An OLM can support SRL by promoting reflection to raise awareness of understanding or developing skills, which can help planning and decision-making [ 6 ]. To date robots have not aimed to support the development of SRL processes. The benefits of a personalised robotic tutor may motivate and engage students to utilise SRL process in the learning activity. We adopt an OLM as the basis for the personalisation as this is a simple and intuitive way of displaying to the learners their developing skills; an OLM allows us to ensure that the learner has all relevant information on which to base their reflections and SRL processes upon. We hypothesise that more personalised and adapted scaffolding of SRL processes via OLM will lead to higher learning gain and improvement in SRL processes. Results show that more personalised and adaptive scaffolding lead to a greater indication of SRL processes and higher learning gains. This paper is organised as follows. First we present the relevant background research on educational robots, SRL, and OLM (Sect. 2). After describing the methodology employed to conduct the study, we present the results based on the domain tests and activity logs of the learning activity. We conclude the paper with a discussion of how a robotic tutor can scaffold SRL (Sect. 5) and how this can impact child learning (Sect. 6). 2 Related Work 2.1 Educational Robots There is an increasing amount of research that investigates how robots can be of benefit in an educational context. (...truncated)


This is a preview of a remote PDF: https://link.springer.com/content/pdf/10.1007%2Fs12369-017-0430-y.pdf

Aidan Jones, Susan Bull, Ginevra Castellano. “I Know That Now, I’m Going to Learn This Next” Promoting Self-regulated Learning with a Robotic Tutor, International Journal of Social Robotics, 2017, pp. 1-16, DOI: 10.1007/s12369-017-0430-y