Katherine Forbes-Riley

Publications35
h-index18
Citations1,501
Highly Influential Citations78
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Spoken Versus Typed Human and Computer Dialogue Tutoring
TLDR
We compare spoken versus typed tutoring dialogues for learning gains and time on task, and also measured the correlations of learning gains with dialogue features. Expand
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Predicting Student Emotions in Computer-Human Tutoring Dialogues
TLDR
We examine the utility of speech and lexical features for predicting student emotions in computer-human spoken tutoring dialogues. Expand
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Benefits and challenges of real-time uncertainty detection and adaptation in a spoken dialogue computer tutor
TLDR
We evaluate the performance of a spoken dialogue system that provides substantive dynamic responses to automatically detected user affective states. Expand
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Recognizing student emotions and attitudes on the basis of utterances in spoken tutoring dialogues with both human and computer tutors
TLDR
We present an empirical study investigating the feasibility of recognizing student state in two corpora of spoken tutoring dialogues, one with human tutor, and one with a computer tutor. Expand
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Designing and evaluating a wizarded uncertainty-adaptive spoken dialogue tutoring system
TLDR
We describe the design and evaluation of two different dynamic student uncertainty adaptations in wizarded versions of a spoken dialogue tutoring system. Expand
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Using system and user performance features to improve emotion detection in spoken tutoring dialogs
TLDR
In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Expand
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Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
TLDR
We examine the utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues. Expand
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Responding to Student Uncertainty During Computer Tutoring: An Experimental Evaluation
TLDR
This paper evaluates dialogue-based student performance in a controlled experiment using versions of a tutoring system with and without automatic adaptation to the student affective state of uncertainty. Expand
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Adapting to Multiple Affective States in Spoken Dialogue
TLDR
We evaluate a wizard-of-oz spoken dialogue system that adapts to multiple user affective states in real-time: user disengagement and uncertainty. Expand
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Investigating Human Tutor Responses to Student Uncertainty for Adaptive System Development
TLDR
We use a i¾?2analysis on our spoken dialogue tutoring corpus to investigate dependencies between uncertain student answers and 9 dialogue acts the human tutor uses in his response to these answers. Expand
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