Teaching Machine Learning to Design Students

  • Authors:

  • Bram Vlist;Rick Westelaken;Christoph Bartneck;Jun Hu;Rene Ahn;Emilia Barakova;Frank Delbressine;Loe Feijs

  • Affiliations:

  • Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB;Department of Industrial Design, Eindhoven University of Technology, Eindhoven, The Netherlands 5600MB

  • Venue:
  • Edutainment '08 Proceedings of the 3rd international conference on Technologies for E-Learning and Digital Entertainment
  • Year:
  • 2008

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Abstract

Machine learning is a key technology to design and create intelligent systems, products, and related services. Like many other design departments, we are faced with the challenge to teach machine learning to design students, who often do not have an inherent affinity towards technology. We successfully used the Embodied Intelligence method to teach machine learning to our students. By embodying the learning system into the Lego Mindstorm NXT platform we provide the student with a tangible tool to understand and interact with a learning system. The resulting behavior of the tangible machines in combination with the positive associations with the Lego system motivated all the students. The students with less technology affinity successfully completed the course, while the students with more technology affinity excelled towards solving advanced problems. We believe that our experiences may inform and guide other teachers that intend to teach machine learning, or other computer science related topics, to design students.