Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
Experiences in Implementing Constraint-Based Modeling in SQL-Tutor
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
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
COMET: A Collaborative Tutoring System for Medical Problem-Based Learning
IEEE Intelligent Systems
Expanding the Plausible Solution Space for Robustness in an Intelligent Tutoring System
ITS '08 Proceedings of the 9th international conference on Intelligent Tutoring Systems
A Comparison of Model-Tracing and Constraint-Based Intelligent Tutoring Paradigms
International Journal of Artificial Intelligence in Education
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
An intelligent tutoring system for visual classification problem solving
Artificial Intelligence in Medicine
A comparative analysis of cognitive tutoring and constraint-based modeling
UM'03 Proceedings of the 9th international conference on User modeling
Expanding the Space of Plausible Solutions in a Medical Tutoring System for Problem-Based Learning
International Journal of Artificial Intelligence in Education
METEOR: medical tutor employing ontology for robustness
Proceedings of the 16th international conference on Intelligent user interfaces
Employing UMLS for generating hints in a tutoring system for medical problem-based learning
Journal of Biomedical Informatics
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Problem based learning is becoming widely popular as an effective teaching method in medical education. Paying individual attention to a small group of students in medical problem-based learning (PBL) can place burden on the workload of medical faculty whose time is very costly. Intelligent tutoring systems offer a cost effective alternative in helping to train the students, but they are typically prone to brittleness and the knowledge acquisition bottleneck. Existing tutoring systems accept a small set of approved solutions for each problem scenario stored into the system. Plausible student solutions that lie outside the scope of the explicitly encoded ones receive little acknowledgment from the system. Tutoring hints are also confined to the knowledge space of the approved solutions, leading to brittleness in the tutoring approach. We report the clinical reasoning gains off a tutoring system for medical PBL that employs and represents the widely available medical knowledge source UMLS as the domain ontology. We exploit the structure of the concept hierarchy to expand the plausible solution space and generate hints based on the problem solving context. Evaluation of student learning outcomes led to highly significant learning gains (Mann-Whitney, p