Correlations between dialogue acts and learning in spoken tutoring dialogues
نویسندگان
چکیده
منابع مشابه
Correlations between dialogue acts and learning in spoken tutoring dialogues
We examine correlations between dialogue behaviors and learning in tutoring, using two corpora of spoken tutoring dialogues: a human-human corpus and a human-computer corpus. To formalize the notion of dialogue behavior, we manually annotate our data using a tagset of student and tutor dialogue acts relative to the tutoring domain. A unigram analysis of our annotated data shows that student lea...
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We examine correlations between dialogue characteristics and learning in two corpora of spoken tutoring dialogues: a human-human corpus and a humancomputer corpus, both of which have been manually annotated with dialogue acts relative to the tutoring domain. The results from our human-computer corpus show that the presence of student utterances that display reasoning, as well as the presence of...
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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...
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Speech recognition errors have been shown to negatively correlate with user satisfaction in evaluations of task-oriented spoken dialogue systems. In the domain of tutorial dialogue systems, however, where the primary evaluation metric is student learning, there has been little investigation of whether speech recognition errors also negatively correlate with learning. In this paper we examine co...
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Human tutors detect and respond to student emotional states, but current machine tutors do not. Our preliminary machine learning experiments involving transcription, emotion annotation and automatic feature extraction from our human-human spoken tutoring corpus indicate that the spoken tutoring system we are developing can be enhanced to automatically predict and adapt to student emotional states.
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ژورنال
عنوان ژورنال: Natural Language Engineering
سال: 2006
ISSN: 1351-3249,1469-8110
DOI: 10.1017/s1351324906004165