This paper discusses a machine learning framework that uses extraction, classification, and generalization techniques to classify students according to their cognitive and behavioral learning patterns and to categorize tutoring strategies of expert human tutors. A great deal of the discussion focuses on the use of reinforcement learning techniques, specifically the ?-greedy and temporal difference TD(0) methods in deriving tutoring policies over a class of students. Future works will deal on incremental learning and modification of learned policies while the tutor performs on-line in real-time, and extracting and learning the way expert tutors execute their tutoring activities.