“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)