When Children Teach a Robot to Write: An Autonomous Teachable Humanoid Which Uses Simulated Handwriting

This article presents a novel robotic partner which children can teach handwriting. The system relies on the learning by teaching paradigm to build an interaction, so as to stimulate meta-cognition, empathy and increased self-esteem in the child user. We hypothesise that use of a humanoid robot in such a system could not just engage an unmotivated student, but could also present the opportunity for children to experience physically-induced benefits encountered during human-led handwriting interventions, such as motor mimicry. By leveraging simulated handwriting on a synchronised tablet display, a nao humanoid robot with limited fine motor capabilities has been configured as a suitably embodied handwriting partner. Statistical shape models derived from principal component analysis of a dataset of adult-written letter trajectories allow the robot to draw purposefully deformed letters. By incorporating feedback from user demonstrations, the system is then able to learn the optimal parameters for the appropriate shape models. Preliminary in situ studies have been conducted with primary school classes to obtain insight into children’s use of the novel system. Children aged 6-8 successfully engaged with the robot and improved its writing to a level which they were satisfied with. The validation of the interaction represents a significant step towards an innovative use for robotics which addresses a widespread and socially meaningful challenge in education.

Learning By Collaborative Teaching : An Engaging Multi-Party CoWriter Activity

Thibault Lucien Christian Asselborn, Wafa Monia Benkaouar Johal, Pierre Dillenbourg, Jauwairia Nasir

This paper presents the design of a novel and engaging collaborative learning activity for handwriting where a group of participants simultaneously tutor a Nao robot. This activity was intended to take advantage of both collaborative learning and the learning by teaching paradigm to improve children’s meta-cognition (perception of their own skills). Multiple engagement probes were integrated into the activity as a first step towards fostering long term interactions. As a lot of research targets social interactions, the goal here was to determine whether an engagement strategy focused on the task could be as, or more efficient than one focused on social interactions and participants’ introspection. To that effect, two engagement strategies were implemented. They differed in content but used the same multi-modal design in order to increase participants’ meta-cognitive reflection, once on the task and performances, and once on participants’ enjoyment and emotions. Both strategies were compared to a baseline by probing and assessing engagement at the individual and group level, along the behavioural, emotional and cognitive dimensions, in a between subject experiment with 12 groups of children. The experiments showed that the collaborative task pushed the children to adapt their manner of writing to the group, even though the adopted solution was not always correct. Furthermore, there was no significant difference between the strategies in terms of behaviour on task (behavioural engagement), satisfaction (emotional engagement) or performance (cognitive engagement) as the group dynamics had a stronger impact on the outcome of the collaborative teaching task. Therefore, the task and social engagement strategies can be considered as efficient in the context of collaboration. © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

2019

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

Thibault Lucien Christian Asselborn, Wafa Monia Benkaouar Johal, Pierre Dillenbourg

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.

FRONTIERS MEDIA SA2020

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.