A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.

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

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

Exploring Spatial Perspective Taking in Human-Robot Interaction

Elmira Yadollahi

Humans often rely on their perspective taking skills to thrive within the world's complex relations and connections. An adequate understanding of others' spatial perspectives can increase the quality of the interaction, not only perceptually but also cognitively. This thesis is dedicated to exploring children and adults' spatial perspective taking abilities emerging from the interaction with embodied and virtual robots in different contexts. While most previous approaches were limited to particular circumstances and targeted adults, the proposed approach developed a cognitive model incorporated in agents to foster perspective taking abilities in different contexts. The developments of the model also detail the processes used by humans to infer spatial connections from other's viewpoints and it is adaptable to add or remove processes based on the context of interaction.First, this thesis explores different interaction modalities and cognitive abilities through user studies with children. Each interaction outlines a set of components and processes required to develop a perspective taking model for robots and agents. The platform developed for the first study aims at evaluating the effect of a robot's non-verbal gestures, such as pointing, on children's joint attention during reading activity. The second study evaluates children's perspective adaptation to the robot in the context of collaborative activity. We expanded our observations to include children's first perspective choice, how they tried to accommodate the robot's perspective, and how they updated their mental model during the interaction. The third study explores children's spatial perspective taking abilities using game-based interaction and non-verbal channels.Inspired by previous studies, the thesis proposes a cognitive model that uses automatic and cognitive controlled processes to generate behaviors and decisions for the robot. The model focuses on processes linked with taking spatial perspectives and can be integrated into any agent architecture that deals with decision making and reasoning. The agent processes are then adapted to a new interaction scenario. Finally, the thesis evaluates the model through a user study with adult participants and within a virtual platform, a drastic change from the exploratory study designs caused by the pandemic. The final studies are designed as two-player games with two virtual robots that interact with each other in two contexts of competition and cooperation. In the competitive version of the game, the robot guided by the human plays against the robot equipped with the perspective taking model, while in the cooperative version, the robots guide each other to win the game as a team. We performed two between-subject studies with more than 180 adults to evaluate the participants' perception of a robot with complete perspective taking abilities, compared to one with limited abilities. Participants were more influenced by the robot's perspective taking abilities in the cooperative game compared to the competitive one, which was reflected in their ratings of the robot's intelligence and game fun. Experimental results on the model evaluation can open up future possibilities for exploring links between the perspective developments in children through cooperation and competition. Furthermore, the model can be extended to study other perceptional, cognitive, and affective dimensions, such as prosocial behavior and and transparency.

EPFL2021