Students entering a new field must learn to speak the specialized language of that field. Previous research using automated measures of word overlap has found that students who modify their language to align more closely to a tutor’s language show larger overall learning gains. We present an alternative approach that assesses syntactic as well as lexical alignment in a corpus of human-computer tutorial dialogue. We found distinctive patterns differentiating high and low achieving students. Our high achievers were most likely to mimic their own earlier statements and rarely made mistakes when mimicking the tutor. Low achievers were less likely to reuse their own successful sentence structures, and were more likely to make mistakes when trying to mimic the tutor. We argue that certain types of mimicking should be encouraged in tutorial dialogue systems, an important future research direction.
|Title of host publication||Artificial Intelligence in Education|
|Subtitle of host publication||15th International Conference, AIED 2011, Auckland, New Zealand, June 28 – July 2011|
|Editors||Gautam Biswas, Susan Bull, Judy Kay, Antonija Mitrovic|
|Number of pages||8|
|Publication status||Published - 2011|
|Name||Lecture Notes in Computer Science|
|Publisher||Springer Berlin Heidelberg|