Problem-solving strategies have been investigated in various informatics education contexts. However, no substantial research has yet been conducted on the problem-solving behavior of students in the field of Machine Learning (ML). This study aims to bridge this gap by analyzing the self-directed problem-solving processes employed by students in grades 8 to 10 (n=93) when developing decision trees as classification models. A digital multi-touch puzzle game and a custom-developed toolchain were utilized to visually capture and subsequently analyze students’ gameplay behaviors using quantitative content analysis techniques. The results of this study indicate that learners within the examined age group predominantly employ exploratory problem-solving strategies in the self-directed construction of decision trees. In contrast, structured approaches are employed much less frequently and demonstrate lower persistence, yet they are significantly more correlated with successful game completion. These findings underscore the necessity of developing learning environments that promote the application and facilitate the persistence of structured problem-solving strategies, enabling learners to engage with the functioning and development of decision trees in a systematic and purposeful manner.