MachineLearnAthon: An Action-Oriented Machine Learning Didactic Concept

TL;DR

The paper addresses the challenge of teaching ML to students with diverse backgrounds by proposing MachineLearnAthon, an action-oriented, challenge-driven didactic concept. It derives its design from a systematic literature review of ML teaching formats and three didactic principles: action orientation, constructivism, and problem orientation, implemented via micro-lectures and real-world datasets. The contributions include a concrete course structure, timeline, and integration guidance for university curricula, as well as a call for an evaluation framework and multi-institution deployments. The approach aims to broaden data literacy, foster interdisciplinary collaboration, and bridge the gap between theory and practical, responsible ML deployment in industry.

Abstract

Machine Learning (ML) techniques are encountered nowadays across disciplines, from social sciences, through natural sciences to engineering. The broad application of ML and the accelerated pace of its evolution lead to an increasing need for dedicated teaching concepts aimed at making the application of this technology more reliable and responsible. However, teaching ML is a daunting task. Aside from the methodological complexity of ML algorithms, both with respect to theory and implementation, the interdisciplinary and empirical nature of the field need to be taken into consideration. This paper introduces the MachineLearnAthon format, an innovative didactic concept designed to be inclusive for students of different disciplines with heterogeneous levels of mathematics, programming and domain expertise. At the heart of the concept lie ML challenges, which make use of industrial data sets to solve real-world problems. These cover the entire ML pipeline, promoting data literacy and practical skills, from data preparation, through deployment, to evaluation.

Figures (2)

• Figure 1: Process of literature review
• Figure 2: Timeline of the ML course