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

• URL: http://arxiv.org/abs/2411.05424v1
• Date: Fri, 08 Nov 2024 09:16:05 GMT
• Title: ICE-T: A Multi-Faceted Concept for Teaching Machine Learning
• Authors: Hendrik Krone, Pierre Haritz, Thomas Liebig,
• Abstract summary: We take a look at didactic principles that are employed for teaching computer science, define criteria, and evaluate a selection of prominent existing platforms, tools, and games. We criticize the approach of portraying Machine Learning mostly as a black-box and the resulting missing focus on creating an understanding of data, algorithms, and models. We present a concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles.
• Score: 2.9685635948300004
• License:
• Abstract: 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.

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