The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
The role of initiative in tutorial dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems
International Journal of Artificial Intelligence in Education
Comparing Linguistic Features for Modeling Learning in Computer Tutoring
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
CMCL '11 Proceedings of the 2nd Workshop on Cognitive Modeling and Computational Linguistics
Talk like an electrician: student dialogue mimicking behavior in an intelligent tutoring system
AIED'11 Proceedings of the 15th international conference on Artificial intelligence in education
Predicting change in student motivation by measuring cohesion between tutor and student
IUNLPBEA '11 Proceedings of the 6th Workshop on Innovative Use of NLP for Building Educational Applications
Study of the phenomenon of phonetic convergence thanks to speech dominoes
COST'10 Proceedings of the 2010 international conference on Analysis of Verbal and Nonverbal Communication and Enactment
SIGDIAL '12 Proceedings of the 13th Annual Meeting of the Special Interest Group on Discourse and Dialogue
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In this paper we examine whether the student-to-tutor convergence of lexical and speech features is a useful predictor of learning in a corpus of spoken tutorial dialogs. This possibility is raised by the Interactive Alignment Theory, which suggests a connection between convergence of speech features and the amount of semantic alignment between partners in a dialog. A number of studies have shown that users converge their speech productions toward dialog systems. If, as we hypothesize, semantic alignment between a student and a tutor (or tutoring system) is associated with learning, then this convergence may be correlated with learning gains. We present evidence that both lexical convergence and convergence of an acoustic/prosodic feature are useful features for predicting learning in our corpora. We also find that our measure of lexical convergence provides a stronger correlation with learning in a human/computer corpus than did a previous measure of lexical cohesion.