Cellulo: Versatile Handheld Robots for Education
Ayberk Özgür, Séverin Lemaignan, Wafa Johal, Maria Beltran, Manon Briod, Léa Pereyre, Francesco Mondada, Pierre Dillenbourg
In this article, we present Cellulo, a novel robotic platform that investigates three new ideas for robotics in education: designing the robots to be versatile and generic tools (instead of tools that focus on STEM teaching only), blending robots into the classroom by designing them to be pervasive objects and by creating tight interactions with (already pervasive) paper; and finally considering the practical constraints of real classrooms at every stage of the design. Our platform results from these considerations and builds on a unique combination of technologies: groups of handheld haptic-enabled robots, tablets and activity sheets printed on regular paper. The robots feature holonomic motion, haptic feedback capability and high accuracy localization through a microdot pattern overlaid on top of the activity sheets, while remaining affordable (robots cost about 125 Euros at the prototype stage) and classroom-friendly. We present the platform and report on our first interaction studies, involving about 230 children.
Adaptive robot language tutoring based on Bayesian knowledge tracing and predictive decision-making
Thorsten Schodde, Kirsten Bergmann, Stefan Kopp
In this paper, we present an approach to adaptive language tutoring in child-robot interaction. The approach is based on a dynamic probabilistic model that represents the inter-relations between the learner’s skills, her observed behavior in tutoring interaction, and the tutoring action taken by the system. Being implemented in a robot language tutor, the model enables the robot tutor to trace the learner’s knowledge and to decide which skill to teach next and how to address it in a game-like tutoring interaction. Results of an evaluation study are discussed demonstrating how participants in the adaptive tutoring condition successfully learned foreign language words.
Growing Mindset with a Social Robot Peer
Hae Won Park, Rinat Rosenberg-Kima, Maor Rosenberg, Goren Gordon, Cynthia Breazeal
Mindset has been shown to have a large impact on people’s academic, social, and work achievements. A growth mindset, i.e., the belief that success comes from effort and grit, is a better indicator of higher achievements as compared to a fixed mindset, i.e., the belief that things are set and cannot be changed. Interventions aimed at promoting a growth mindset in children range from teaching about the brain’s ability to learn and change to playing computer games that grant brain-points for effort rather than success. This work explores a novel paradigm to foster a growth mindset in young children where they play a puzzle-solving game with a peer-like social robot. The social robot is fully-autonomous and programmed with behaviors suggestive of it having either a growth mindset or a neutral mindset as it plays puzzle games with the child. We measure the mindset of children before and after interacting with the peer-like robot, in addition to measuring their problem-solving behavior when faced with a challenging puzzle. We found that children who played with a growth-mindset robot 1) self-reported having a stronger growth mindset, and 2) tried harder during a challenging task, as compared to children who played with the neutral-mindset robot. These results suggest that interacting with peer-like social robot with a growth mindset can promote the same mindset in children.
Give Me a Break! Personalized Timing Strategies to Promote Learning in Robot-Child Tutoring
Aditi Ramachandran, Chien-Ming Huang, Brian Scassellati
A common practice in education to accommodate the short attention spans of children during learning is to provide them with non-task breaks for cognitive rest. Holding great promise to promote learning, robots can provide these breaks at times personalized to individual children. In this work, we investigate personalized timing strategies for providing breaks to young learners during a robot tutoring interaction. We build an autonomous robot tutoring system that monitors student performance and provides break activities based on a personalized schedule according to performance. We conduct a fi eld study to explore the eff ects of diff erent strategies for providing breaks during tutoring. By comparing a fixed timing strategy with a reward strategy (break timing personalized to performance gains) and a refocus strategy (break timing personalized to performance drops), we show that the personalized strategies promote learning gains for children more effectively than the fixed strategy. Our results also show immediate bene fits in enhancing efficiency and accuracy in completing math questions after personalized breaks, providing evidence for the restorative effects of the breaks when administered at the right time.
Windfield: Learning Wind Meteorology with Handheld Haptic Robots
Ayberk Özgür, Wafa Johal, Francesco Mondada, Pierre Dillenbourg
This article presents a learning activity and its user study involving the Cellulo platform, a novel versatile robotic tool designed for education. In order to show the potential of Cellulo in the classroom as part of standard curricular activities, we designed a learning activity called Windfield that aims to teach the atmospheric formation mechanism of wind to early middle school children. The activity involves a didactic sequence, introducing the Cellulo robots as hot air balloons and enabling children to feel the wind force through haptic feedback. We present the user study, designed in the form of a real hour-long lesson, conducted with 24 children in 8 groups who had no prior knowledge in the subject. Collaborative metrics within groups and individual performances about the learning of key concepts were measured with only the hardware and software integrated in the platform in a completely automated manner. The results show that almost all participants showed learning of symmetric aspects of wind formation while about half showed learning of asymmetric vectoral aspects that are more complex.
(Ir)relevance of Gender? On the Influence of Gender Stereotypes on Learning with a Robot
Natalia Reich-Stiebert, Friederike Eyssel
Education research has documented a trend that reflects gender-based differences in the choice of fields of study. This, in turn, contributes to an imbalance in the representation of men and women in particular professions: In the school context, female teachers predominantly teach stereotypically female areas of study like socials sciences, whereas male teachers are mainly represented in stereotypically male domains like mathematics. Research further provides evidence for the fact that this gender-stereotyped division of labor in education and higher education significantly impacts students’ learning and motivation. Would gender-related stereotypes also bias learning processes with robots? This is plausible in light of the fact that as social robots become steadily more popular in learning settings. Thus, should the next generation of education robots be ‘gendered’ and what impact would robot gender have on task performance, particularly in the context of a gender-stereotypical human-robot interaction (HRI) task? To investigate these issues, we examined the influence of robot gender on learning when completing either stereotypically female or stereotypically male learning tasks. 120 participants (60 females and 60 males) completed either stereotypically female or stereotypically male tasks with the support of an instructor robot for which we experimentally manipulated robot gender. The manipulation check indicated that participants recognized the robot’s alleged gender correctly. Importantly, our results suggest that prevailing gender stereotypes associated with learning do not apply to robots that perform gender-stereotypical tasks. Interestingly, though, our findings indicate that a mismatch of robot gender and task gender typicality leads to increased willingness to engage in prospective learning processes with the robot. Our results will be discussed with respect to future research on HRI and learning, and with regard to practical implications associated with the introduction of robots into educational settings.
Event Timeslots (1)
Tue, Mar 7
-
Robots in Education