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

TL;DR

This paper introduces ICE-T, a comprehensive didactic framework designed to improve the teaching of machine learning by emphasizing understanding of data, algorithms, and models beyond black-box approaches.

Contribution

The paper proposes the ICE-T concept, extending didactic principles to enhance ML education through intermodal transfer and explanatory thinking.

Findings

Evaluation of existing platforms shows a focus on black-box portrayal.

ICE-T framework addresses gaps in understanding data and algorithms.

The concept aims to guide educators and platform creators in improving ML teaching.

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.