The music listening preferences of children have been the subject of numerous studies, intending to inform the design of music recommender systems to better cater to children’s specific needs. Most of these studies are centered around genre, while few explore other traits of songs to capture listening behavior. In this research, we propose song structure, the arrangement of songs into sections, as a novel lens to analyze children’s music preferences. We create an extension from the popular LFM2b dataset to the Genius Lyrics dataset. Using this dataset, we group song interactions of users according to the educational level of children aged 12 through 17 and adults, and analyze and compare how song sections play a role in their listening behavior, based on the share of similarly structured songs in their listening data. To identify similar song structures, we cluster songs on their song structure fingerprints, extracted from their lyrics. We find no salient differences in the preferences of the children’s age groups, but we do find preliminary indications that adults interact less with songs characterized by the presence of lyrical Hooks (the most catchy parts of songs) than children and more with songs that are focused around Choruses. Although these insights can be used directly by recommender systems, our findings can be used as a springboard for future audio-based research to focus on not only lyrical, but also instrumental Hooks and their relation to children’s music preferences.