Identifying struggling students in real-time provides a virtual learning environment with an opportunity to intervene meaningfully with supports aimed at improving student learning and engagement. In this paper, we present a detailed analysis of quit prediction modeling in students playing a learning game called Physics Playground. From the interaction log data of the game, we engineered a comprehensive set of aggregated features of varying levels of granularity and trained individualized level-specific models and a single level-agnostic model. Contrary to our initial expectation, our results suggest that a level-agnostic model achieves superior predictive performance. We enhanced this model further with level-related and student-related features, leading to a moderate increase in AUC. Visualizing this model, we observe that it is based on high-level intuitive features that are generalizable across levels. This model can now be used in future work to automatically trigger cognitive and affective supports to motivate students to pursue a game level until completion. [For the full proceedings, see ED593090.]