1 research outputs found
ARtonomous: Introducing Middle School Students to Reinforcement Learning Through Virtual Robotics
Typical educational robotics approaches rely on imperative programming for
robot navigation. However, with the increasing presence of AI in everyday life,
these approaches miss an opportunity to introduce machine learning (ML)
techniques grounded in an authentic and engaging learning context. Furthermore,
the needs for costly specialized equipment and ample physical space are
barriers that limit access to robotics experiences for all learners. We propose
ARtonomous, a relatively low-cost, virtual alternative to physical,
programming-only robotics kits. With ARtonomous, students employ reinforcement
learning (RL) alongside code to train and customize virtual autonomous robotic
vehicles. Through a study evaluating ARtonomous, we found that middle-school
students developed an understanding of RL, reported high levels of engagement,
and demonstrated curiosity for learning more about ML. This research
demonstrates the feasibility of an approach like ARtonomous for 1) eliminating
barriers to robotics education and 2) promoting student learning and interest
in RL and ML.Comment: In Proceedings of Interaction Design and Children (IDC '22