Procedural help in Andes: generating hints using a Bayesian network student model
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Responding to Unexpected Student Utterances in CIRCSIM-Tutor v.3: Analysis of Transcripts
Proceedings of the Eleventh International Florida Artificial Intelligence Research Society Conference
Generating and Revising Hierarchical Multi-turn Text Plans in an ITS
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Context dependent planning in a machine tutor (artificial intelligence, teaching systems, meno-tutor)
Broadening input understanding in a language-based intelligent tutoring system
Broadening input understanding in a language-based intelligent tutoring system
User interfaces and help systems: from helplessness to intelligent assistance
Artificial Intelligence Review
An Analysis of Multiple Tutoring Protocols
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
A Practical Student Model in an Intelligent Tutoring System
ICTAI '99 Proceedings of the 11th IEEE International Conference on Tools with Artificial Intelligence
Plan-based dialogue management in a physics tutor
ANLC '00 Proceedings of the sixth conference on Applied natural language processing
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Automated tutoring dialogues for training in shipboard damage control
SIGDIAL '01 Proceedings of the Second SIGdial Workshop on Discourse and Dialogue - Volume 16
Designing an Intelligent Coach for a Collaborative Concept-Mapping Learning Environment
Advances in Blended Learning
Determining tutorial remediation strategies from a corpus of human-human tutoring dialogues
ENLG '07 Proceedings of the Eleventh European Workshop on Natural Language Generation
Artificial Intelligence in Medicine
Preference orderings for regulatory concepts in environmental management
TELE-INFO'10 Proceedings of the 9th WSEAS international conference on Telecommunications and informatics
Hints, learning styles, and learning orientations
TELE-INFO'10 Proceedings of the 9th WSEAS international conference on Telecommunications and informatics
Partial orderings for ranking help functions
ACIIDS'10 Proceedings of the Second international conference on Intelligent information and database systems: Part II
Dominance relations in rough sets approximations for assessing students knowledge
AIKED'11 Proceedings of the 10th WSEAS international conference on Artificial intelligence, knowledge engineering and data bases
Towards the prediction of user actions on exercises with hints based on survey results
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
Automated decision making based on weak orderings
International Journal of Intelligent Information and Database Systems
Persuasive dialogues in an intelligent tutoring system for medical diagnosis
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part II
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Hinting is an important tutoring tactic in one-on-one tutoring, used when the tutor needs to respond to an unexpected answer from the student. To issue a follow-up hint that is pedagogically helpful and conversationally smooth, the tutor needs to suit the hinting strategy to the student's need while making the strategy fit the high level tutoring plan and the tutoring context. This paper describes a study of the hinting strategies in a corpus of human tutoring transcripts and the implementation of these strategies in a dialogue-based intelligent tutoring system, CIRcslM-Tutor v. 2. We isolated a set of hinting strategies from human tutoring transcripts. We describe our analysis of these strategies and a model for choosing among them based on domain knowledge, the type of error made by the student, the focus of the tutor's question, and the conversational history. We have tested our model with two classes totaling 74 medical students. Use of this extended model of hinting increases the percentage of questions that students are able to answer for themselves rather than needing to be told.