Understanding and debugging novice programs
Artificial Intelligence - Special issue on artificial intelligence and learning environments
Partial constraint satisfaction
Artificial Intelligence - Special volume on constraint-based reasoning
Information Retrieval
Uncertainty in Constraint Satisfaction Problems: a Probalistic Approach
ECSQARU '93 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Guided programming and automated error analysis in an intelligent Prolog tutor
International Journal of Human-Computer Studies
Valued constraint satisfaction problems: hard and easy problems
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Adding weights to constraints in intelligent tutoring systems: does it improve the error diagnosis?
EC-TEL'11 Proceedings of the 6th European conference on Technology enhanced learning: towards ubiquitous learning
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Problems for which multiple solution strategies are possible can be challenging for intelligent tutors. These kinds of problems are often the norm in exploratory learning environments which allow students to develop solutions in a creative manner without many restrictions imposed by the problem solving interface. How can intelligent tutors determine a student's intention in order to give appropriate feedback for problems with multiple, quite different solutions? This paper focuses on improving the diagnosis capabilities of constraint-based intelligent tutors with respect to supporting problems with multiple possible solution strategies. An evaluation study showed that by applying a soft-computing technique (a probabilistic approach for constraint satisfaction problems), the diagnostic accuracy of constraint-based intelligent tutors can be improved.