Tailoring object descriptions to a user's level of expertise
Computational Linguistics - Special issue on user modeling
Dynamics in document design: creating text for readers
Dynamics in document design: creating text for readers
Integration and synchronization of input modes during multimodal human-computer interaction
Proceedings of the ACM SIGCHI Conference on Human factors in computing systems
Building natural language generation systems
Building natural language generation systems
SNePS: a logic for natural language understanding and commonsense reasoning
Natural language processing and knowledge representation
Generating Natural Language Aggregations Using a Propositional Representation of Sets
Proceedings of the Fifteenth International Florida Artificial Intelligence Research Society Conference
Cognitive Status and Form of Reference in Multimodal Human-Computer Interaction
Proceedings of the Seventeenth National Conference on Artificial Intelligence and Twelfth Conference on Innovative Applications of Artificial Intelligence
Computational Linguistics
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
Intentional influences on object redescriptions in dialogue: evidence from an empirical study
A fast and portable realizer for text generation systems
ANLC '97 Proceedings of the fifth conference on Applied natural language processing
Linguistic theories in efficient multimodal reference resolution: an empirical investigation
Proceedings of the 10th international conference on Intelligent user interfaces
A corpus-based analysis for the ordering of clause aggregation operators
COLING '02 Proceedings of the 19th international conference on Computational linguistics - Volume 1
MUP: the UIC standoff markup tool
SIGDIAL '02 Proceedings of the 3rd SIGdial workshop on Discourse and dialogue - Volume 2
ACL '05 Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
The politeness effect: Pedagogical agents and learning outcomes
International Journal of Human-Computer Studies
Expert tutoring and natural language feedback in intelligent tutoring systems
Expert tutoring and natural language feedback in intelligent tutoring systems
Spoken Versus Typed Human and Computer Dialogue Tutoring
International Journal of Artificial Intelligence in Education
Responding to Student Uncertainty in Spoken Tutorial Dialogue Systems
International Journal of Artificial Intelligence in Education
Effective feedback content for tutoring complex skills
Human-Computer Interaction
Expert vs. Non-expert Tutoring: Dialogue Moves, Interaction Patterns and Multi-utterance Turns
CICLing '07 Proceedings of the 8th International Conference on Computational Linguistics and Intelligent Text Processing
Natural Language Generation for Intelligent Tutoring Systems: a case study
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
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
Can a Polite Intelligent Tutoring System Lead to Improved Learning Outside of the Lab?
Proceedings of the 2007 conference on Artificial Intelligence in Education: Building Technology Rich Learning Contexts That Work
Adapting to when students game an intelligent tutoring system
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Evaluating the effectiveness of tutorial dialogue instruction in an exploratory learning context
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
Simple but effective feedback generation to tutor abstract problem solving
INLG '08 Proceedings of the Fifth International Natural Language Generation Conference
Facilitating mental modeling in collaborative human-robot interaction through adverbial cues
SIGDIAL '11 Proceedings of the SIGDIAL 2011 Conference
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To investigate whether more concise Natural Language feedback improves learning, we developed two Natural Language generators (DIAG-NLP1 and DIAG-NLP2), to provide feedback in an Intelligent Tutoring System that teaches troubleshooting. We systematically evaluated them in a three way comparison that included the original system, which generates overly repetitive feedback. We found that DIAG-NLP2, the generator which intuitively produces the best, corpus-based language, does engender the most learning. Distinguishing features of the more effective feedback are: it obeys Grice's maxim of brevity, it is more directive and uses a specific type of referring expressions. Interestingly, simpler ways of restructuring the original repetitive feedback as done in DIAG-NLP1, such as exploiting the hierarchical structure of the domain, were not effective. Since the design of interfaces to Intelligent Tutoring Systems often includes verbal feedback, we suggest that: if the number of different contexts in which verbal feedback is provided is high, such feedback should be based on corpus studies, and generated by techniques more sophisticated than template filling.