KERMIT: A Constraint-Based Tutor for Database Modeling
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
The Architecture of Why2-Atlas: A Coach for Qualitative Physics Essay Writing
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
An Intelligent Tutoring System Incorporating a Model of an Experienced Human Tutor
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
CADE-18 Proceedings of the 18th International Conference on Automated Deduction
Analysis of mixed natural and symbolic language input in mathematical dialogs
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
The Andes Physics Tutoring System: Five Years of Evaluations
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Generating references to parts of recursively structured objects
INLG '06 Proceedings of the Fourth International Natural Language Generation Conference
DiaWOz-II: a tool for wizard-of-Oz experiments in mathematics
KI'06 Proceedings of the 29th annual German conference on Artificial intelligence
Verifying and invalidating textbook proofs using scunak
MKM'06 Proceedings of the 5th international conference on Mathematical Knowledge Management
Handling errors in mathematical formulas
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
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Teaching problem-solving in formal domains aims at two purposes: 1) increasing the students' skill in addressing the problem in a goal-oriented way, and 2) increasing their competence in expressing themselves formally. In a dialog, suitable tutoring strategies addressing both issues may be quite delicate, especially when meeting formally inaccurate or even faulty statements which nevertheless are useful from the problem-solving perspective. Based on evidence from Wizard-of-Oz studies on human tutoring in mathematical theorem proving, we have developed a model for addressing formally inaccurate statements within a problem-solving context. Based on an error recognition and correction module, the model performs a conceptually-motivated error categorization and generates a suitable response.