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Current speech-enabled Intelligent Tutoring Systems do not model student question behavior the way human tutors do, despite evidence indicating the importance of doing so. Our study examined a corpus of spoken tutorial dialogues collected for development of ITSpoke, an Intelligent Tutoring Spoken Dialogue System. The authors extracted prosodic, lexical, syntactic, and student and task dependent information from student turns. Results of running 5-fold cross validation machine learning experiments using AdaBoosted C4.5 decision trees show prediction of student question-bearing turns at a rate of 79.7%. The most useful features were prosodic, especially the pitch slope of the last 200 milliseconds of the student turn. Student pre-test score was the most-used feature. Findings indicate that using turn-based units is acceptable for incorporating question detection capability into practical Intelligent Tutoring Systems.