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.

本研究针对机器学习教学中存在的理解缺失问题，提出了一种名为ICE-T的多维度教学概念，以促进学生对数据、算法和模型的理解。通过评估现有的教学平台和工具，研究显示ICE-T能够改进机器学习的教学效果，帮助教育工作者更有效地传授相关知识。

ICE-T：一种多维度的机器学习教学概念