Windfield Learning Wind Meteorology with Handheld Haptic Robots

• Published in: 2017 12th ACM/IEEE International Conference on Human-Robot Interaction (HRI pp. 156 - 165
• Main Authors: Özgür, Ayberk, Johal, Wafa, Mondada, Francesco, Dillenbourg, Pierre
• Format: Conference Proceeding
• Language: English
• Published: New York, NY, USA ACM 06.03.2017
• Series: ACM Conferences
• Subjects: Applied computing > Education > Interactive learning environments; Collaboration; Computer systems organization > Embedded and cyber-physical systems > Robotics; Education; Haptic interfaces; Human-centered computing > Human computer interaction (HCI) > HCI design and evaluation methods > User studies; Human-centered computing > Human computer interaction (HCI) > Interaction devices > Haptic devices; Human-Robot Interaction; Meteorology; Robot kinematics; Robot sensing systems; Robots for Learning; Tangible Robots; human-robot interaction; robots for learning; haptic interfaces; tangible robots
• ISBN: 9781450343367; 1450343368
• ISSN: 2167-2148
• DOI: 10.1145/2909824.3020231

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 a 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 vectorial aspects that are more complex.