Neuro Fuzzy Reasoner for Student Modeling
ICALT '06 Proceedings of the Sixth IEEE International Conference on Advanced Learning Technologies
Social Interaction in Robotic Agents Emulating the Mirror Neuron Function
IWINAC '07 Proceedings of the 2nd international work-conference on Nature Inspired Problem-Solving Methods in Knowledge Engineering: Interplay Between Natural and Artificial Computation, Part II
Teaching Machine Learning to Design Students
Edutainment '08 Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment
The reflective transformative design process
CHI '09 Extended Abstracts on Human Factors in Computing Systems
My Sparring Partner Is a Humanoid Robot
IWINAC '09 Proceedings of the 3rd International Work-Conference on The Interplay Between Natural and Artificial Computation: Part II: Bioinspired Applications in Artificial and Natural Computation
AdMoVeo: A Robotic Platform for Teaching Creative Programming to Designers
Edutainment '09 Proceedings of the 4th International Conference on E-Learning and Games: Learning by Playing. Game-based Education System Design and Development
Expressing and interpreting emotional movements in social games with robots
Personal and Ubiquitous Computing
From spreading of behavior to dyadic interaction—A robot learns what to imitate
International Journal of Intelligent Systems
| Hi-index | 0.00 |
The present day society requires specialists with multidisciplinary knowledge and skills. We discuss the possibilities to educate professionals that design intelligent products and systems as a result of a competency based education. In particular this paper features a teaching method that makes the students use intelligent algorithms that control robot behavior in a way that the robot can solve design problems with practical relevance, and reach human to machine interaction that includes machine intelligence. The outcomes of 14 cases done over 4 years showed that most students were able to integrate the required competencies in a synergistic way. Within 80 hours they could master the technical complexity of controlling a robot through a neural learning algorithm in well-grounded design cases.