Introduction to machine learning with robots and playful learning

Inspired by explaining machine learning ideas in children’s
books, we developed an alternative approach to introduce su-
pervised, unsupervised, and reinforcement learning using a
block-based programming language in combination with the
benefits of educational robotics. Instead of using blocks as
high-end APIs to access AI cloud services or to reproduce the
machine learning algorithms, we use blocks as a means to put
the learner “in the algorithm’s shoes”. We adapt the training
of neural networks, Q-learning, and k-means algorithms in a
design and format suitable for children and equip the student
with the hands-on tools for playful experimentation. The chil-
dren learn direct supervision modifying the weights in neural
networks and immediately observing the effects on the simu-
lated robot. Following the ideas of constructionism, they ex-
perience in practice how the algorithms and underlying ma-
chine learning concepts work. We conducted and evaluated
this approach with children from primary, middle, and high
school. The children of all age groups perceived the topics
very easy to moderately hard to grasp. Younger learners no-
ticed the direct supervision challenging, whereas Q-learning
and k-means algorithms were much more accessible. The
vast majority of high school children could cope with all top-
ics without particular difficulties.