Making large-scale support vector machine learning practical
Advances in kernel methods
Correlations between dialogue acts and learning in spoken tutoring dialogues
Natural Language Engineering
A classification of dialogue actions in tutorial dialogue
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
Discovering Tutorial Dialogue Strategies with Hidden Markov Models
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
My science tutor: A conversational multimedia virtual tutor for elementary school science
ACM Transactions on Speech and Language Processing (TSLP)
DISCUSS: a dialogue move taxonomy layered over semantic representations
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
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Asking questions in a context relevant manner is a critical behavior for intelligent tutoring systems; however even within a single pedagogy there may be numerous valid strategies. This paper explores the use of supervised ranking models to rank candidate questions in the context of tutorial dialogues. By training models on individual and aggregate judgments from experienced tutors, we learn to reproduce individual and average preferences in questioning. Analysis of our models' performance across different tutors highlights differences in individual teaching preferences and illustrates the impact of surface form, semantic and pragmatic features for modeling variations in tutoring styles. This work has implications for dialogue system design and provides a natural starting point towards creating tunable and customizable tutorial dialogue interactions.