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Spoken Versus Typed Human and Computer Dialogue Tutoring
It is found that changing the modality from text to speech caused large differences in the learning gains, time and superficial dialogue characteristics of human tutoring, but for computer tutoring it made less difference.
Computing Discourse Semantics: The Predicate-Argument Semantics of Discourse Connectives in D-LTAG
The unique contribution of this paper lies in showing how compositional rules and anaphora resolution interact within the D-LTAG syntax-semantic interface to yield their semantic interpretations, with multi-component syntactic trees sometimes being required.
Predicting Student Emotions in Computer-Human Tutoring Dialogues
The utility of speech and lexical features for predicting student emotions in computer-human spoken tutoring dialogues is examined and the results of machine learning experiments using these features alone or in combination to predict various categorizations of the annotated student emotions are compared.
D-LTAG System: Discourse Parsing with a Lexicalized Tree-Adjoining Grammar
An implementation of a discourse parsing system for alexicalized Tree-Adjoining Grammar for discourse, specifying the integration of sentence and discourse level processing on the assumption that the compositional aspects of semantics at the discourse level parallel those at the sentence level.
Using system and user performance features to improve emotion detection in spoken tutoring dialogs
In this study, we incorporate automatically obtained system/user performance features into machine learning experiments to detect student emotion in computer tutoring dialogs. Our results show a
Predicting Emotion in Spoken Dialogue from Multiple Knowledge Sources
The utility of multiple types of turn-level and contextual linguistic features for automatically predicting student emotions in human-human spoken tutoring dialogues is examined and the intelligent tutoring spoken dialogue system developed can be enhanced to automatically predict and adapt to student emotions.
Adapting to Student Uncertainty Improves Tutoring Dialogues
It is shown that affect-adaptive computer tutoring can significantly improve performance on learning efficiency and user satisfaction and only the basic adaptive system shows a positive correlation between learning and user perception of decreased uncertainty.