Code Puzzles can be an engaging way to learn programming concepts, but getting stuck in a puzzle can be discouraging when no help or feedback is available. Teachers and facilitators can alleviate this problem in a classroom setting, but it can be hard for teachers to keep track of who needs help and who is likely to resolve their problem on their own, especially in a large classroom. This work is a step toward helping teachers optimize their time by automatically gauging which students may benefit from an intervention at any given time. We use information about the bugs present in student code to predict which students are more likely to abandon the puzzle or take too long in solving it. Ultimately, we envisions that teachers could use these predictions to make decisions about whom they should help next, and how.