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The CoWriter activity involves a child in a rich and complex interaction where he has to teach handwriting to a robot. The robot must convince the child it needs his help and it actually learns from his lessons. To keep the child engaged, the robot must learn at the right rate, not too fast otherwise the kid will have no opportunity for improving his skills and not too slow otherwise he may loose trust in his ability to improve the robot’ skills. We tested this approach in real pedagogic/therapeutic contexts with children in difficulty over repeated long sessions (40-60 min). Through 3 different case studies, we explored and refined experimental designs and algorithms in order for the robot to adapt to the troubles of each child and to promote their motivation and self-confidence. We report positive observations, suggesting commitment of children to help the robot, and their comprehension that they were good enough to be teachers, overcoming their initial low confidence with handwriting.
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.
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.
We present the design approach and evaluation of our proto- type called “Ranger”. Ranger is a robotic toy box that aims to motivate young children to tidy up their room. We evalu- ated Ranger in 14 families with 31 children (2-10 years) using the Wizard-of-Oz technique. This case study explores two different robot behaviors (proactive vs. reactive) and their impact on children’s interaction with the robot and the tidy- ing behavior. The analysis of the video recorded scenarios shows that the proactive robot tended to encourage more playful and explorative behavior in children, whereas the reactive robot triggered more tidying behavior. Our find- ings hold implications for the design of interactive robots for children, and may also serve as an example of evaluating an early version of a prototype in a real-world setting.
While robots have been popular as a tool for STEM teaching, the use of robots in other learning scenarios is novel. The field of HRI has started to report on how to make effective robots usable in educational contexts. However, many challenges remain. For instance, which interaction strategies aid learning, and which hamper learning? How can we deal with the current technical limitations of robots? Answering these and other questions requires a multidisciplinary effort, including contributions from pedagogy, developmental psychology, (computational) linguistics, artificial intelligence and HRI, among others. This abstract provides a brief overview of the current state-of-the-art in social robots designed for learning and describes the aims of the Robots for Learning (R4L) workshop in bringing together a multidisciplinary audience for furthering the development of market-ready educational robots.
We present a study on the impact of unexpected robot behaviors on the perception of a robot by children and their subsequent engagement in a playful interaction based on a novel ”domino” task. We propose an original analysis methodology which blends behavioral cues and reported phenomenological perceptions into a compound index. While we found only a limited recognition of the different misbehaviors of the robot that we attribute to the age of the child participants (4-5 years old), interesting findings include a sustained engagement level, an unexpectedly low level of attribution of higher cognitive abilities and a negative correlation between anthropomorphic projections and actual behavioral engagement.