We present an analysis of student language\ud input in a corpus of tutoring dialogue\ud in the domain of symbolic differentiation.\ud Our focus on procedural tutoring\ud makes the dialogue comparable to collaborative\ud problem-solving (CPS). Existing\ud CPS models describe the process of\ud negotiating plans and goals, which also\ud fits procedural tutoring. However, we\ud provide a classification of student utterances\ud and corpus annotation which shows\ud that approximately 28% of non-trivial student\ud language in this corpus is not accounted\ud for by existing models, and addresses\ud other functions, such as evaluating\ud past actions or correcting mistakes. Our\ud analysis can be used as a foundation for\ud improving models of tutoring dialogue
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