We are developing a system that automatically collects the history of students' program editing, compilation, and execution history during programming exercises, recognizes the history and its temporal changes, and detects impasses in the exercises. Using the results, we have also developed a system to support individual advice for students who are stuck in a programming exercise. In this study, we use machine learning to detect whether a student is still stuck or not after the next 10 minutes from 20 minutes of automatically collected exercise activity history. We employed several machine learning methods to identify the impasse using the training data from the exercise history collected in previous exercises. Although the dataset is relatively small, an F value of 0.95 was obtained as the impasse detection rate by learning with a decision tree.