Unsupervised machine learning is a complex and abstract topic, posing challenges for student comprehension. Considering the considerable growth of relevance the topic of machine learning has seen in the past years, teaching it effectively has become ever-so important. Analogy-based teaching approaches offer a potential solution by mapping unfamiliar machine learning concepts to familiar real-world ideas. This paper investigates how analogies can improve the understanding of unsupervised learning, a rather relevant field within machine learning. Contributions include a collection of analogies for teaching unsupervised ML, an expert-based evaluation of these analogies’ quality, and a student-centred assessment of analogy-based teaching.
The findings from the expert evaluation show a consensus on the effectiveness of several analogies and highlight which analogies might be less effective. The findings from the student assessment suggest that the analogical explanations are more effective than 'generic' explanations and suggest that students have a higher satisfaction while learning through analogies.
We conclude that well-crafted analogies can enhance student understanding in unsupervised machine learning. The study’s insights can guide educators in integrating analogies to make unsupervised learning more accessible.