Child-Robot Spatial Arrangement in a Learning by Teaching Activity

In this paper, we present an experiment in the context of a child-robot interaction where we study the influence of the child-robot spatial arrangement on the child’s focus of attention and the perception of the robot’s performance. In the “Co-Writer learning by teaching” activity, the child teaches a Nao robot how to handwrite. Usually only face-to-face spatial arrangements are tested in educational child robot interactions, but we explored two spatial conditions from Kendon’s F-formation, the side-by-side and the face-to-face formations in a within subject experiment. We estimated the gaze behavior of the child and their consistency in grading the robot with regard to the robot’s progress in writing. Even-though the demonstrations provided by children were not different between the two conditions (i.e. the robot’s learning didn’t differ), the results showed that in the side-by-side condition children tended to be more indulgent with the robot’s mistakes and to give it better feedback. These results highlight the influence of experimental choices in child-robot interaction

Bringing letters to life: handwriting with haptic-enabled tangible robots

In this paper, we present a robotic approach to improve the teaching of handwriting using the tangible, haptic-enabled and classroom-friendly Cellulo robots. Our efforts presented here are in line with the philosophy of the Cellulo platform: we aim to create a ready-to-use tool (i.e. a set of robot-assisted activities) to be used for teaching handwriting, one that is to coexist harmoniously with traditional tools and will contribute new added values to the learning process, complementing existing teaching practices. To maximize our potential contributions to this learning process, we focus on two promising aspects of handwriting: the visual perception and the visual-motor coordination. These two aspects enhance in particular two sides of the representation of letters in the mind of the learner: the shape of the letter (the grapheme) and the way it is drawn, namely the dynamics of the letter (the ductus). With these two aspects in mind, we do a detailed content analysis for the process of learning the representation of letters, which leads us to discriminate the specific skills involved in letter representation. We then compare our robotic method with traditional methods as well as with the combination of the two methods, in order to discover which of these skills can benefit from the use of Cellulo. As handwriting is taught from age 5, we conducted our experiments with 17 five-year-old children in a public school. Results show a clear potential of our robot-assisted learning activities, with a visible improvement in certain skills of handwriting, most notably in creating the ductus of the letters, discriminating a letter among others and in the average handwriting speed. Moreover, we show that the benefit of our learning activities to the handwriting process increases when it is used after traditional learning methods. These results lead to the initial insights into how such a tangible robotic learning technology may be used to create cost-effective collaborative scenarios for the learning of handwriting

An Adaptive Teachable Robot For Encouraging Teamwork

Social robots used in education can take different roles, including tutor robots and peer robots. Peer robots (also called teachable robots) take the role of a novice in a teaching interaction while the students take the role of the teacher. Teachable robots leverage learning by teaching, which has been shown in prior research to increase the students’ learning effort and time spent on the learning activity, leading to enhanced student learning. The concept of teachable robots has previously been applied for one-to-one interaction, however, to date, few studies use teachable robots in a group setting. In this thesis, we developed an adaptive learning algorithm for a teachable robot that encourages a group of students to discuss their thoughts and teaching decisions during the tutoring session. We hypothesize that the robot's encouragement of group discussion can enhance the social engagement of group members, leading to improved task engagement, learning and enjoyment. The robot adapts to the students' talking activity and adjusts the frequency and type of encouragement. The robot uses reinforcement learning to maximise interaction between the students. The proposed approach was validated through a series of studies. The first pilot study was performed in an elementary school and observed the interactions between groups of students and teachable robots. The main study investigated the feasibility of an adaptive encouraging robot in a remote setting. We recruited 68 adults, who worked together in pairs online on a web application called Curiosity Notebook to teach a humanoid robot about the classification of rocks and minerals. We measured social engagement based on the communication between group-mates, while the metric for task engagement was generated based on the users’ activities in the Curiosity Notebook. The results show that the adaptive robot was successful in creating more dialogue between group members and in increasing task engagement, but did not affect learning or enjoyment. Over time, the adaptive robot was also able to encourage both members to contribute more equally to the conversation