Children Teach Handwriting to a Social Robot with Different Learning Competencies

As robots are entering into educational fields to enhance children's learning, it becomes relevant to explore different methods of learning in the area of child-robot interaction. In this article, we present an autonomous educational system incorporating a social robot to enhance children's handwriting skills. The system provides a one-to-one learning scenario based on thelearning-by-teachingapproach where a tutor-child assess the handwriting skills of a learner-robot. The robot's writing was generated by an algorithm incorporating human-inspired movements and could reproduce a set of writing errors. We tested the system by conducting two multi-session studies. In the first 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 of the learner-robot. The second 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 learnt with the robot that exhibits learning competency and children's learning and perceptions of the robot changed as interactions unfold, confirming the need for longitudinal studies. This research supports that the contrasting learning competencies of social robots can impact children's learning differently in peer-learning scenarios.

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

Do Children Perceive Whether a Robotic Peer is Learning or Not?

Shruti Chandra, Pierre Dillenbourg, Ana Paiva, Hang Yin

Social robots are being used to create better educational scenarios, thereby fostering children's learning. In the work presented here, we describe an autonomous social robot that was designed to enhance children's handwriting skills. Exploiting the benefits of the learning-by-teaching method, the system provides a scenario in which a child acts as a teacher and corrects the handwriting difficulties of the robotic agent. To explore the children's perception towards this social robot and the effect on their learning, we have conducted a multi-session study with children that compared two contrasting competencies in the robot: 'learning'vs 'non-learning' and presented as two conditions in the study. The results suggest that the children learned more in the learning condition compared with the non-learning condition and their learning gains seem to be affected by their perception of the robot. The results did not lead to any significant differences in the children's perception of the robot in the first two weeks of interaction. However, by the end of the 4th week, the results changed. The children in the learning condition gave significantly higher writing ability and overall performance scores to the robot compared with the non-learning condition. In addition, the change in the robot's learning capabilities did not show to affect their perceived intelligence, likability and friendliness towards it.

ASSOC COMPUTING MACHINERY2018

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].