Dialog Convergence and Learning
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Cohesion Relationships in Tutorial Dialogue as Predictors of Affective States
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Dealing with interpretation errors in tutorial dialogue
SIGDIAL '09 Proceedings of the SIGDIAL 2009 Conference: The 10th Annual Meeting of the Special Interest Group on Discourse and Dialogue
The impact of interpretation problems on tutorial dialogue
ACLShort '10 Proceedings of the ACL 2010 Conference Short Papers
BEETLE II: a system for tutoring and computational linguistics experimentation
ACLDemos '10 Proceedings of the ACL 2010 System Demonstrations
Beetle II: an adaptable tutorial dialogue system
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
Towards effective tutorial feedback for explanation questions: a dataset and baselines
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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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.