A bi-level physics student diagnostic utilizing cognitive models for an intelligent tutoring system
A bi-level physics student diagnostic utilizing cognitive models for an intelligent tutoring system
Student assessment using Bayesian nets
International Journal of Human-Computer Studies - Special issue: real-world applications of uncertain reasoning
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
Conceptual and Meta Learning During Coached Problem Solving
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Reasoning about Systems of Physics Equations
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Minimally Invasive Tutoring of Complex Physics Problem Solving
ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Andes: A Coached Problem Solving Environment for Physics
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Using Computer Algebra for Rapid Development of ITS Components in Engineering
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Using a Probabilistic Student Model to Control Problem Difficulty
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Fading and Deepening: The Next Steps for Andes and other Model-Tracing Tutors
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
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Andes, an intelligent tutoring system for Newtonian physics, provides an environment for students to solve quantitative physics problems. Andes provides immediate correct/incorrect feedback to each student entry during problem solving. When a student enters an equation, Andes must (1) determine quickly whether that equation is correct, and (2) provide helpful feedback indicating what is wrong with the student's entry. To address the former, we match student equations against a pregenerated list of correct equations. To address the latter, we use the pre-generated equations to infer what equation the student may have been trying to enter, and generate hints based on the discrepancies. This paper describes the representation of equations and the procedures Andes uses to perform these tasks.