Our research goal is to investigate whether previous findings and methods in the area of tutorial dialogue can be generalized across dialogue corpora that differ in domain (mechanics versus electricity in physics), modality (spoken versus typed), and tutor type (computer versus human). We first present methods for unifying our prior coding and analysis methods. We then show that many of our prior findings regarding student dialogue behaviors and learning not only generalize across corpora, but that our methodology yields additional new findings. Finally, we show that natural language processing can be used to automate some of these analyses.

this article were presented at the 1999 meeting of the American Educational Research Association, Montreal and at the 1999 meeting of the American Psychological Association, Boston. The authors thank the reviewers of this article for their critical feedback and valuable suggestions

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Over the past several decades of research, consistent differences in academic achievement among students of various racial groups and of different nations have been well-documented. More recent models in accounting for cross-national differences in