Metacognition and Learning in Spoken Dialogue Computer Tutoring

• Published in: Intelligent Tutoring Systems pp. 379 - 388
• Main Authors: Forbes-Riley, Kate, Litman, Diane
• Format: Book Chapter
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2010
• Series: Lecture Notes in Computer Science
• Subjects: automatic speech recognition and correctness and uncertainty annotation; correlations; learning; metacognition; natural language processing; spoken dialogue computer tutor
• ISBN: 3642133878; 9783642133879
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-642-13388-6_42

We investigate whether four metacognitive metrics derived from student correctness and uncertainty values are predictive of student learning in a fully automated spoken dialogue computer tutoring corpus. We previously showed that these metrics predicted learning in a comparable wizarded corpus, where a human wizard performed the speech recognition and correctness and uncertainty annotation. Our results show that three of the four metacognitive metrics remain predictive of learning even in the presence of noise due to automatic speech recognition and automatic correctness and uncertainty annotation. We conclude that our results can be used to inform a future enhancement of our fully automated system to track and remediate student metacognition and thereby further improve learning.