Logo
ScienceStack
Table of Contents
Fetching ...
Sign In

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

Hendrik Krone, Pierre Haritz, Thomas Liebig

TL;DR

A concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles is presented, believing that planners of learning units, creators of learning platforms and educators can improve on teaching ML.

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.

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

TL;DR

A concept that covers intermodal transfer, computational and explanatory thinking, ICE-T, as an extension of known didactic principles is presented, believing that planners of learning units, creators of learning platforms and educators can improve on teaching ML.

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.

Paper Structure

This paper contains 12 sections, 6 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background & Related Work
  3. Intermodal Transfer
  4. Computational Thinking
  5. Explanatory Thinking
  6. Related Tools & Games
  7. Methodology & Results
  8. RQ1: To what extent do existing games, digital tools and platforms for teaching machine learning implement the facets of intermodal transfer, as well as computational and explanatory thinking?
  9. RQ2: How can a process model for teaching Machine Learning be derived from a Data Mining process model?
  10. RQ3: How can the presented didactic principles be combined and applied?
  11. Conclusion

Figures (6)

  • Figure 1: Intermodal Transfer via the EIS principle: Enactive - Iconic - Symbolic.
  • Figure 2: Computational Thinking via UMC: Use - Modify - Create.
  • Figure 3: CRoss Industry Standard Process for Data Mining.
  • Figure 4: Promotion of Explanatory Thinking Standard Process for Machine Learning.
  • Figure 5: The three facets of the ICE-T principle: Intermodal Transfer, Computational Thinking, and Explanatory Thinking.
  • ...and 1 more figures