The use of robots in education has increased in the last decade in a remarkable manner. This tendency has been even more relevant in pre-university education, where robots have shown to be a perfect tool for teaching in STEM areas. Previously, robots have been mainly used as mechatronic elements to learn basic electronics or basic programming skills, insufficient for present day robotics.
Recent progress in the teaching of computational intelligence, or its use for identifying the learning process, has also been very remarkable, with powerful and simple tools to introduce teachers and students into fields like machine learning, computer vision or natural language processing. Computational Intelligence in real or simulated robotics is thus a key topic for all educational levels in the years to come.
The goal of this Research Topic is to create a collection of papers that summarize the state of the art on the advances in computational intelligence for educational robotics. Formal and reliable didactical approaches towards teaching intelligent robotics, which should have been tested with students at classes, and examples of how computational intelligence can support and enhance learning and teaching are welcome. Experiences in machine learning, computer vision, or any other specific subject of computational intelligence that has been tested in real or simulated robots will be considered; with the aim of establishing a starting point towards future curricula of intelligent robotics at several levels of education. The requested experiences could come from primary, secondary and high school, but also from university degrees, which can provide very interesting examples that could inspire previous levels. In this sense, new middleware infrastructures experiences at classes are encouraged, as well as those based on the application of robot simulators like ROS, LCM, ZeroMQ, and others.
The expected topics for this Research Topic include, but are not limited to:
• Computational intelligence experiences in educational robotics for pre-university education
• Computational intelligence experiences in educational robotics for university education
• Educational robots & Machine Learning
• Educational robots & Computer Vision
• Intelligent Robotics Curriculum
• Intelligent Robotics to foster STEM areas
• Educational robot simulators
• ROS in education
• Teaching teachers for computational intelligence
• Easing education of Computational intelligence through robots
• Local, Remote and simulated laboratories for education of Computational intelligence with robots
• Experiences with low-cost setups regarding education of Computational Intelligence with robots
• Fostering learner’s autonomy in robotics education and Computational Intelligence
• Easy to use / no install / take-home / low-cost solutions for Computational Intelligence education
Topic Editor Francisco Bellas is a Founder of MINT, a company which manufactures and commercialises the educational robot "Robobo". All other Topic Editors declare no competing interests with regard to the Research Topic subject.
The use of robots in education has increased in the last decade in a remarkable manner. This tendency has been even more relevant in pre-university education, where robots have shown to be a perfect tool for teaching in STEM areas. Previously, robots have been mainly used as mechatronic elements to learn basic electronics or basic programming skills, insufficient for present day robotics.
Recent progress in the teaching of computational intelligence, or its use for identifying the learning process, has also been very remarkable, with powerful and simple tools to introduce teachers and students into fields like machine learning, computer vision or natural language processing. Computational Intelligence in real or simulated robotics is thus a key topic for all educational levels in the years to come.
The goal of this Research Topic is to create a collection of papers that summarize the state of the art on the advances in computational intelligence for educational robotics. Formal and reliable didactical approaches towards teaching intelligent robotics, which should have been tested with students at classes, and examples of how computational intelligence can support and enhance learning and teaching are welcome. Experiences in machine learning, computer vision, or any other specific subject of computational intelligence that has been tested in real or simulated robots will be considered; with the aim of establishing a starting point towards future curricula of intelligent robotics at several levels of education. The requested experiences could come from primary, secondary and high school, but also from university degrees, which can provide very interesting examples that could inspire previous levels. In this sense, new middleware infrastructures experiences at classes are encouraged, as well as those based on the application of robot simulators like ROS, LCM, ZeroMQ, and others.
The expected topics for this Research Topic include, but are not limited to:
• Computational intelligence experiences in educational robotics for pre-university education
• Computational intelligence experiences in educational robotics for university education
• Educational robots & Machine Learning
• Educational robots & Computer Vision
• Intelligent Robotics Curriculum
• Intelligent Robotics to foster STEM areas
• Educational robot simulators
• ROS in education
• Teaching teachers for computational intelligence
• Easing education of Computational intelligence through robots
• Local, Remote and simulated laboratories for education of Computational intelligence with robots
• Experiences with low-cost setups regarding education of Computational Intelligence with robots
• Fostering learner’s autonomy in robotics education and Computational Intelligence
• Easy to use / no install / take-home / low-cost solutions for Computational Intelligence education
Topic Editor Francisco Bellas is a Founder of MINT, a company which manufactures and commercialises the educational robot "Robobo". All other Topic Editors declare no competing interests with regard to the Research Topic subject.
