Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System

Natalie B. Steinhauser, Gwendolyn E. Campbell, Leanne S. Taylor, Simon Caine, Charlie Scott, Myroslava O. Dzikovska, Johanna D. Moore

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publicationArtificial Intelligence in Education
Subtitle of host publication15th International Conference, AIED 2011, Auckland, New Zealand, June 28 – July 2011
EditorsGautam Biswas, Susan Bull, Judy Kay, Antonija Mitrovic
PublisherSpringer-Verlag GmbH
Number of pages8
ISBN (Electronic)978-3-642-21869-9
ISBN (Print)978-3-642-21868-2
Publication statusPublished - 2011

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Fingerprint Dive into the research topics of 'Talk Like an Electrician: Student Dialogue Mimicking Behavior in an Intelligent Tutoring System'. Together they form a unique fingerprint.

Cite this