ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
This work targets educators and curriculum planners in AI/ML education, offering an incremental improvement to teaching methods.
The paper addresses the challenge of teaching machine learning effectively by critiquing existing educational tools for treating ML as a black-box and proposes ICE-T, a multi-faceted concept based on didactic principles to enhance understanding of data, algorithms, and models.
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 concepts 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.