ITS '02 Proceedings of the 6th International Conference on Intelligent Tutoring Systems
Experiences in Implementing Constraint-Based Modeling in SQL-Tutor
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
ITCC '04 Proceedings of the International Conference on Information Technology: Coding and Computing (ITCC'04) Volume 2 - Volume 2
Designing an Ontology-Based Intelligent Tutoring Agent with Instant Messaging
ICALT '05 Proceedings of the Fifth IEEE International Conference on Advanced Learning Technologies
Modeling individual and collaborative problem-solving in medical problem-based learning
User Modeling and User-Adapted Interaction
A Knowledge Acquisition System for Constraint-based Intelligent Tutoring Systems
Proceedings of the 2005 conference on Artificial Intelligence in Education: Supporting Learning through Intelligent and Socially Informed Technology
Enriching Solution Space for Robustness in an Intelligent Tutoring System
Proceedings of the 2007 conference on Supporting Learning Flow through Integrative Technologies
An intelligent tutoring system for visual classification problem solving
Artificial Intelligence in Medicine
METEOR: medical tutor employing ontology for robustness
Proceedings of the 16th international conference on Intelligent user interfaces
Leveraging a domain ontology to increase the quality of feedback in an intelligent tutoring system
ITS'10 Proceedings of the 10th international conference on Intelligent Tutoring Systems - Volume Part I
Employing UMLS for generating hints in a tutoring system for medical problem-based learning
Journal of Biomedical Informatics
Clinical reasoning gains in medical PBL: an UMLS based tutoring system
Journal of Intelligent Information Systems
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The knowledge acquisition bottleneck is a problem pertinent to the authoring of any intelligent tutoring system. Allowing students a broad scope of reasoning and solution representation whereby a wide range of plausible student solutions are accepted by the system, places additional burden on knowledge acquisition. In this paper we present a strategy to alleviate the burden of knowledge acquisition for building a tutoring system for medical problem-based learning (PBL). The Unified Medical Language System (UMLS) is deployed as domain ontology and information structure in the ontology is exploited to make intelligent inferences and expand the domain model. Using these inferences and expanded domain model, the tutoring system is able to accept a broader range of plausible student solutions that lie beyond the scope of explicitly encoded solutions. We describe the development of a tutoring system prototype and report the evaluation of system correctness in accepting such plausible solutions. The system evaluation indicates an average accuracy of 94.59 % when compared against human domain experts, who agreed among themselves with a statistical agreement based on Pearson Correlation Coefficient of 0.48 and p