Machine Learning is becoming a standard part of Computer Science curriculums at universities. This paper aims to contribute to the education of Machine Learning in Computer Science, specifically through teaching concepts related to Gradient Descent (GD) through analogies. First, concepts related to Gradient Descent were collected through the use of academic textbooks, and analogies were created based on the definitions found. These analogies were then evaluated by experts, scoring the analogies on Target Concept Coverage, Mapping Strength, and Metaphoricity. The analogies that scored highest on a mean average were then used in an A/B survey distributed amongst Computer Science students that had not followed any Machine Learning course. One group was given the concept definitions, the other both the definitions and the analogies. The learning proficiency was measured, and no statistically significant result was found. In the end, this research explores the possibilities of creating analogies to explain machine learning concepts, and provides a modular framework for evaluating quality and measuring effectiveness of analogies.

This research paper looks into the influence of domain specificity on the understanding and motivation of first-year computer science students learning different concepts in supervised machine learning. Two types of domains were chosen for the analogies, the general domain and the gaming domain, the latter being the more specific one. These were evaluated in two phases. First, experts rated the analogies based on different metrics. Then, a user study was carried out using A/B testing to measure knowledge gain and motivation when exposed to the analogies. Results from the user evaluation show no statistically significant differences in terms of understanding for domain-specific or general analogies. Motivation, similarly show little difference when comparing both domains. The findings suggest that if analogies are helpful when it comes to understanding a topic, as long as the learner knows the domain, they do not play a big role.

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.

This study examines the effect of analogies on conceptual understanding of machine learning (ML) loss functions, and the motivation to learn in first-year bachelor computer science students. For a set of 10 ML loss functions, analogies were generated and evaluated by 15 experts. 3 of these analogies were subsequently tested with 22 students. The results show no conclusive evidence for improvement in understanding and motivation to learn. The study outlines a general strategy for evaluation of analogies on student understanding and motivation. The study further provides 10 expert-rated analogies, 3 of which have been tested with students.

Machine Learning education faces significant challenges due to the abstract and mathematically-complex nature of fundamental models, such as Multilayer Perceptrons (MLPs). This paper investigates the effectiveness of conceptual metaphors and analogies as pedagogical tools to improve novice learner's understanding of key MLP concepts. Using large language models, we generated a set of analogies for core MLP topics. These analogies were then evaluated by experts to assess their quality, followed by a user study with novice learners employing a between-subject A/B test comparing analogy-based explanations to formal definitions. Although the study found no statistically significant improvement in knowledge gain or engagement that could be attributed to analogy-based explanations, trends suggest potential benefits in learner confidence and motivation. The research contributes a curated set of expert-evaluated analogies for ML education and discusses methodology limitations and directions for future work. This study highlights both the promise and complexity of integrating analogy-based teaching approaches into ML education.