ICE-T: A Multi-Faceted Concept for Teaching Machine Learning

The topics of Artificial intelligence (AI) and especially Machine Learning (ML) are increasingly making their way into educational
curricula. To facilitate the access for students, a variety of platforms, visual tools, and digital games are already being used to introduce ML con-
cepts and strengthen the understanding of how AI works. We take a look
at didactic principles that are employed for teaching computer science,
define criteria, and, based on those, evaluate a selection of prominent
existing platforms, tools, and games. Additionally, we criticize the approach of portraying ML mostly as a black-box and the resulting missing
focus on creating an understanding of data, algorithms, and models that
come with it. To tackle this issue, we present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an
extension of known didactic principles. With our multi-faceted concept,
we believe that planners of learning units, creators of learning platforms
and educators can improve on teaching ML.