Causal model progressions as a foundation for intelligent learning environments
Artificial Intelligence - Special issue on artificial intelligence and learning environments
A study of problem solving activities in a hypermedia representation
Journal of Educational Multimedia and Hypermedia
Journal of Artificial Intelligence in Education
Representation of Models for Expert Problem Solving in Physics
IEEE Transactions on Knowledge and Data Engineering
Building ITSs to be used: lessons learned from the APLUSIX project
Proceedings of the IFIP TC3/WG3.3 Working Conference on Lessons from Learning
KERMIT: A Constraint-Based Tutor for Database Modeling
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Tailoring Feedback by Correcting Student Answers
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
An Empirical Assessment of Comprehension Fostering Features in an Intelligent Tutoring System
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Recasting the feedback debate: benefits of tutoring error detection and correction skills
Recasting the feedback debate: benefits of tutoring error detection and correction skills
Advanced Geometry Tutor: An intelligent tutor that teaches proof-writing with construction
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Integration of a Complex Learning Object in a Web-Based Interactive Learning System
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
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In this paper we present the design of a learning environment in a mathematical domain (elementary combinatorics) where problem solving is based more on modeling than on deduction or calculation. In this approach, we want to provide students with a presentation which is close to the natural language formulations that they tend to give spontaneously, while ensuring a rigorous mathematical reasoning. To do so, we have introduced three modeling levels: first, a mathematical formalization of the students' intuitive process, that we called the constructive method, then a conceptual and computational model that allows mathematical reasoning as well as communication with the student, and finally a presentation consisting of several "machines". We show that, in such a system, error detection is specific. We present an incremental mechanism of error detection. Specific knowledge necessary to detect and explain the errors is organized into a database of error schemas. The system Combien? founded on this research, has been used by university students since 2002.