The Robotics Academy is a project-based teaching and learning environment for undergraduate students. Students’ learning is motivated by the goal of solving a real world problem. In this new project-based learning environment, students of each discipline will learn to teach and mentor the others within the Academy. Finally, all students will receive substantial mentoring from faculty members in the various disciplines as well as from the professional schools.

Goals:
  • By contributing to a multi-disciplinary team each student will gain the experience of being the “resident expert” in their particular field while learning from their teammates’ expertise in other areas.
  • By working with corporations, hospitals, and NASA, students will get a better idea of how the different organizations function.
  • By working in teams, students will learn the importance of leadership and communication skills.
  • By working with students and professors across the country, students will learn how to work effectively over the Internet, an important skill as more and more companies become “global” with engineers working together in a virtual environment.
  • By mentoring and teaching other students, students gain confidence and mastery in the material themselves, as well as promote cross-disciplinary communication.
  • By including pre-service teachers, the engineers learn the importance of working with the education system, and the teachers learn the importance of teaching engineering.
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    News:

    2005/2006 Projects Underway!
    The Robotics Academy is kicking off its fourth year with a couple of new projects underway. Tufts University seniors worked over the summer to get a head start on this years challenges.
    Click here to find out more >>

    Third Annual Race Across Campus!
    On Friday, October 14, the Robotics Academy held the third annual Race Across Campus robotic relay race. Four teams built robots as fast as they could to overcome four different challenges.
    Click here to find out more >>

    This material is based upon work supported by the National Science Foundation under Grant No. 0212046. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation (NSF).
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