Using Evaluation to Shape ITS Design: Results and Experiences with SQL-Tutor
User Modeling and User-Adapted Interaction
An Intelligent SQL Tutor on the Web
International Journal of Artificial Intelligence in Education
Interacting with Inspectable Bayesian Student Models
International Journal of Artificial Intelligence in Education
Proposing metrics of difficulty of domain knowledge using usecase diagrams
Proceedings of the 2008 ACM symposium on Applied computing
Interoperable Competencies Characterizing Learning Objects in Mathematics
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
Widening the Knowledge Acquisition Bottleneck for Constraint-based Tutors
International Journal of Artificial Intelligence in Education
Exploring quality of constraints for assessment in problem solving environments
ITS'12 Proceedings of the 11th international conference on Intelligent Tutoring Systems
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This paper presents an evaluation study that measures the effect of modifying feedback generality in an Intelligent Tutoring System (ITS) based on Student Models. A taxonomy of the tutor domain was used to group existing knowledge elements into plausible, more general, concepts. Existing student models were then used to measure the validity of these new concepts, demonstrating that at least some of these concepts appear to be more effective at capturing what the students learned than the original knowledge elements. We then trialled an experimental ITS that gave feedback at a higher level. The results suggest that it is feasible to use this approach to determine how feedback might be fine-tuned to better suit student learning, and hence that learning curves are a useful tool for mining student models.