Early prediction of student difficulty during long-duration learning activities allows a tutoring system to intervene by providing needed support, such as a hint, or by alerting an instructor. To be effective, these predictions must come early and be highly accurate, but such predictions are difficult for open-ended programming problems. In this work, Recent Temporal Patterns (RTPs) are used in conjunction with Support Vector Machine and Logistic Regression to build robust yet interpretable models for early predictions. We performed two tasks: to predict student success and difficulty during one, open-ended novice programming task of drawing a square-shaped spiral. We compared RTP against several machine learning models ranging from the classic to the more recent deep learning models such as Long Short Term Memory to predict whether students would be able to complete the programming task. Our results show that RTP-based models outperformed all others, and could successfully classify students after just one minute of a 20- minute exercise (students can spend more than 1 hour on it). To determine when a system might intervene to prevent incompleteness or eventual dropout, we applied RTP at regular intervals to predict whether a student would make progress within the next five minutes, reflecting that they may be having difficulty. RTP successfully classified these students needing interventions over 85% of the time, with increased accuracy using data-driven program features. These results contribute significantly to the potential to build a fully data-driven tutoring system for novice programming. [For the full proceedings, see ED599096.]