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.