PARADISE: a framework for evaluating spoken dialogue agents
ACL '98 Proceedings of the 35th Annual Meeting of the Association for Computational Linguistics and Eighth Conference of the European Chapter of the Association for Computational Linguistics
The role of initiative in tutorial dialogue
EACL '03 Proceedings of the tenth conference on European chapter of the Association for Computational Linguistics - Volume 1
Correlations between dialogue acts and learning in spoken tutoring dialogues
Natural Language Engineering
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Balancing Cognitive and Motivational Scaffolding in Tutorial Dialogue
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Dialogue Modes in Expert Tutoring
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Using Hidden Markov Models to Characterize Student Behaviors in Learning-by-Teaching Environments
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Going Beyond the Problem Given: How Human Tutors Use Post-Solution Discussions to Support Transfer
International Journal of Artificial Intelligence in Education - "Caring for the Learner" in honour of John Self
The impact of instructor initiative on student learning: a tutoring study
Proceedings of the 40th ACM technical symposium on Computer science education
Modeling learning patterns of students with a tutoring system using Hidden Markov Models
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Beyond the code-and-count analysis of tutoring dialogues
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
The Influence of Learner Characteristics on Task-Oriented Tutorial Dialogue
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
KSC-PaL: a peer learning agent that encourages students to take the initiative
EdAppsNLP '09 Proceedings of the Fourth Workshop on Innovative Use of NLP for Building Educational Applications
Modeling dialogue structure with adjacency pair analysis and hidden Markov models
NAACL-Short '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics, Companion Volume: Short Papers
Adapting to Student Uncertainty Improves Tutoring Dialogues
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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
Using Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains Modalities
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
To Elicit Or To Tell: Does It Matter?
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
Generating proactive feedback to help students stay on track
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Learning the Structure of Task-Driven Human–Human Dialogs
IEEE Transactions on Audio, Speech, and Language Processing
The impact of task-oriented feature sets on HMMs for dialogue modeling
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
How do they do it? investigating dialogue moves within dialogue modes in expert human tutoring
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
Learner characteristics and dialogue: recognising effective and student-adaptive tutorial strategies
International Journal of Learning Technology
An extensible micro-world for learning in the data networking professions
Information Sciences: an International Journal
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Identifying effective tutorial dialogue strategies is a key issue for intelligent tutoring systems research. Human-human tutoring offers a valuable model tbr identifying effective tutorial strategies, but extracting them is a challenge because of the richness of human dialogue. This article addresses that challenge through a machine learning approach that 1) learns tutorial modes from a corpus of human tutoring, and 2) identifies the statistical relationships between student outcomes and the learned modes. The modeling approach utilizes hidden Markov models (HMMs) to capture the unobservable stochastic structure that is thought to influence the observations, in this case dialogue acts and task actions, that are generated by task-oriented tutorial dialogue. We refer to this unobservable layer as the hidden dialogue state, and interpret it as representing the tutor and students' collaborative intentions. We have applied HMMs to a corpus of annotated task-oriented tutorial dialogue to learn one model for each of two effective human tutors. Significant correlations emerged between the automatically extracted tutoring modes and student learning outcomes. Broadly, the results suggest that HMMs can learn meaningful hidden tutorial dialogue structure. More specifically, the findings point to specific mechanisms within task-oriented tutorial dialogue that are associated with increased student learning. This work has direct applications in authoring data-driven tutorial dialogue system behavior and in investigating the effectiveness of human tutoring.