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We examine a corpus of reflective tutorial dialogues between human tutor and student after the student completed introductory physics problems, to predict when the tutor abstracted from the student's preceding turn or when the tutor specialized from the student's preceding turn. Tutor abstraction occurs when the tutor repeats a segment of the student's turn using more general terms. Tutor specialization occurs when the tutor repeats a segment of the student's turn using more concrete terms. We find that features extracted from the reflective dialogue context produce the most predictive models. Also, the tutor abstracts more often when the student shows signs of working at a very detailed level for awhile, and prompts for specification when the student's responses are imprecise.