Students in open-ended educational games have a number of different pathways that they can select to work productively through a learning activity. Educators and system designers may want to know which of these pathways are most effective for engagement, learning, or other desirable outcomes. In this paper, we investigate which prior jobs and factors are associated with higher rates of student quitting behavior in an educational science exploration game. We use a series of Chi squared analyses to identify the jobs with the highest rates of quitting overall, and we calculate logistic regressions within specific jobs to determine the potential factors that lead to students quitting those jobs. Our analysis revealed that for 23 of the 40 jobs examined, having experience in at least one previous job significantly decreased the chances of students quitting the subsequent job, and that completing specific prior jobs reduces quit rates on specific later jobs. In our discussion, we describe the challenges associated with modeling quitting behavior, and how these analyses could be used to better optimize students’ pathways through the game environment. Specially, guiding students through specific sequences of preliminary jobs before tackling more challenging jobs can improve their engagement and reduce dropout rates, thus optimizing their learning pathways.