Learning by Teaching a Robot: The Case of Handwriting

Robots for education are not limited to support ICT teaching, and they are indeed finding new roles in the classroom. This article reports on such a new paradigm for educative robots, that involves learning by teaching and strong social engagement to help children struggling with handwriting. Our system relies on machine-learning and child-robot interaction with a small humanoid robot, and we present several real-world studies in schools and with occupational therapists that led us to promising initial results.

Learning How to Write with a Social Robot

Shruti Chandra

Learning handwriting is one of the main occupations for children in primary education. It is a complex skill that has been associated with children’s learning and development. Poor handwriting skills affect children negatively concerning their academic performance and motivation. In schools, children with handwriting problems need extra support and one-to-one interventions from teachers. Personalised one-to-one support requires additional resources that most schools generally cannot afford. Therefore, there is a need to explore different methods of learning and contemporary technologies to develop new educational tools to foster children’s learning process of handwriting. The research presented in this thesis addresses the question of - “How to create a social robot that can help children to acquire handwriting skills?". Thus, we looked into three different research domains: education; pedagogic sciences; and social human-robot interaction (HRI). On the one hand, we investigated the types of errors commonly present in children’s handwriting. On the other hand, we needed to explore different educational theories to find alternate learning methods that would maximise children’s learning gains. In addition, we investigated various possibilities for integrating a social robot into educational scenarios and measured its impact on the children’s learning process. This thesis presents five studies that follow an incremental evolution of the research. The first three studies were based on the interaction between two children, in the presence of either a human or a robotic facilitator, with the children performing a collaborative writing activity. We explored the ‘peer-learning’ and ‘peer-tutoring’ methods and compared the differences in terms of interaction behaviour and learning gains. Further, we analysed children’s interpersonal distance and feelings of responsibility towards their peers, both in the presence of a robot and human facilitator. Finally, we examined children’s assessment behaviour towards their peers and self-disclosure towards the robotic facilitator. The outcomes of these first studies provided enough data to generate a taxonomy of children handwriting errors to be used in the subsequent studies. They hinted that the ‘peer-tutoring’ method had a positive influence on children’s learning and corrective feedback to their peers in the presence of the social robot, providing positive support to the applicability of social robots as ‘facilitators’ in handwriting learning. The next step was to study the effects of the robot in the peer-tutoring scenario when assuming the role of a peer, directly interacting and performing a collaborative writing activity with the children. Thus, we developed an educational system with an autonomous social behaviour of a robot exhibiting various levels of learning competencies. The tutor-child provides corrective feedback on the writing of the learner-robot and in turn, enhances his writing skills. The robot’s writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors from the error taxonomy. We tested the system by conducting two longitudinal studies, each of them composed of four sessions. In the fourth study, we assigned the robot two contrasting competencies: ‘learning’ and ‘non-learning’. We measured the differences in children’s learning gains and changes in their perceptions towards the learner-robot. The fifth study followed a similar interaction scenario and research questions, but this time the robot performed three learning competencies: ‘continuous-learning’;’non-learning’ and ‘personalised-learning’. The findings of these studies show that the children improved more with the robot exhibiting learning, continuous-learning and personalised-learning competencies compared to the robot showing non-learning competency. The way children’s learning and perception of the robot evolved over time confirmed the need for multiple sessions for the last two studies. This thesis presents some evidence that social robots can be successfully used as tools for enhancing children’s handwriting skills in peer-learning scenarios.

EPFL2019

Learning (Good Handwriting In Greek) By Teaching (A Humanoid Robot)

Thibault Lucien Christian Asselborn, Wafa Monia Benkaouar Johal, Thanasis Hadzilacos

We report on a follow-up study of the Co-Writer project at EPFL [1]; we confirm their findings, extend the applicability to another language with a different alphabet (Greek) and go into an in-depth qualitative study of the child-robot relationship. The core idea of "learning by teaching" is that the student learns by undertaking to teach what they are supposed to learn which motivates them into ownership of the process. Although a highly effective method [2], there are serious practical obstacles in scaling learning by teaching to larger numbers or extending it to younger learners and to certain skills such as handwriting. This is where we enlist the assistance of a humanoid robot to help a young learner with her handwriting by assuming the role of the cacographer pupil who asks help from its human friend. The robot pretends writing mistakes similar to the child's own; the child recognizes the errors as such even when she would refuse to acknowledge them in her own writing, and makes an extra effort for better writing as the robot's instructor; the robot improves its writing; the child goes through another round of amelioration; and, hopefully, gets in the habit of better writing. In order to extend the original study for the Greek language and alphabet we first needed to collect data from beginner writers, children of 6-9 years. The nine-month study for collecting and evaluating handwriting samples from primary school students in Cyprus in order to examine and describe cacography for the Greek alphabet used a questionnaire, an evaluation sheet for the letter formation and copies of students' notebooks. It involved four primary schools in Limassol, Cyprus and a total of 68 students and 11 teachers. We were thus able to obtain a consensus on what constitutes poor letter writing in Greek. All writing samples were evaluated by teachers and were classified based on Chandra et al. [3] taxonomy. The qualitative research involved three case studies of children who held two sessions each with the humanoid programmable robot NAO. We video recorded the child-robot interactions and assessed their progress in handwriting. In all three cases, albeit not in the same degree, we saw that (a) the child-robot interaction quickly improved; the increasing number of correct remarks from all children in only two meetings shows the comfort that they gained with lab environment and the robot itself and (b) the correctness of children remarks and their comments about letters shows their ability to distinguish the correct from the wrong letters and to justify their answers. Both conclusions confirm the findings of the Co-Writer project [4].

IATED-INT ASSOC TECHNOLOGY EDUCATION & DEVELOPMENT2020

When Positive Perception of the Robot Has No Effect on Learning

Barbara Bruno, Pierre Dillenbourg, Jauwairia Nasir, Utku Norman

Humanoid robots, with a focus on personalised social behaviours, are increasingly being deployed in educational settings to support learning. However, crafting pedagogical HRI designs and robot interventions that have a real, positive impact on participants' learning, as well as effectively measuring such impact, is still an open challenge. As a first effort in tackling the issue, in this paper we propose a novel robot-mediated, collaborative problem solving activity for school-children, called JUSThink, aiming at improving their computational thinking skills. JUSThink will serve as a baseline and reference for investigating how the robot's behaviour can influence the engagement of the children with the activity, as well as their collaboration and mutual understanding while working on it. To this end, this first iteration aims at investigating (i) participants' engagement with the activity (Intrinsic Motivation Inventory-IMI), their mutual understanding (IMI-like) and perception of the robot (Godspeed Questionnaire); (ii) participants' performance during the activity, using several performance and learning metrics. We carried out an extensive user-study in two international schools in Switzerland, in which around 100 children participated in pairs in one-hour long interactions with the activity. Surprisingly, we observe that while a teams' performance significantly affects how team members evaluate their competence, mutual understanding and task engagement, it does not affect their perception of the robot and its helpfulness, a fact which highlights the need for baseline studies and multi-dimensional evaluation metrics when assessing the impact of robots in educational activities.