Artificial intelligence and tutoring systems: computational and cognitive approaches to the communication of knowledge
Distributed rational decision making
Multiagent systems
Causality: models, reasoning, and inference
Causality: models, reasoning, and inference
Using Bayesian Networks to Manage Uncertainty in Student Modeling
User Modeling and User-Adapted Interaction
How to t(r)ap user's mental models
Selected papers of the 8th Interdisciplinary Workshop on Informatics and Psychology: Mental Models and Human-Computer Interaction 2
Learning Dynamic Bayesian Networks
Adaptive Processing of Sequences and Data Structures, International Summer School on Neural Networks, "E.R. Caianiello"-Tutorial Lectures
A Practical Approach to Bayesian Student Modeling
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Two-Phase Updating of Student Models Based on Dynamic Belief Networks
ITS '98 Proceedings of the 4th 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
The Conceptual Helper: An Intelligent Tutoring System for Teaching Fundamental Physics Concepts
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
Student Modelling Based on Belief Networks
International Journal of Artificial Intelligence in Education
A Comparison of Model-Tracing and Constraint-Based Intelligent Tutoring Paradigms
International Journal of Artificial Intelligence in Education
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Review: Student modeling approaches: A literature review for the last decade
Expert Systems with Applications: An International Journal
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Cognitive approaches have been used for student modeling in intelligent tutoring systems (ITSs). Many of those systems have tackled fundamental subjects such as mathematics, physics, and computer programming. The change of the student's cognitive behavior over time, however, has not been considered and modeled systematically. Furthermore, the nature of domain knowledge in specific subjects such as orthopedic surgery, in which pragmatic knowledge could play an important role, has also not been taken into account deliberately. We believe that the temporal dimension in modeling the student's knowledge state and cognitive behavior is critical, especially in such domains. In this paper, we propose an approach for student modeling and diagnosis, which is based on a symbiosis between temporal Bayesian networks and fine-grained didactic analysis. The latter may help building a powerful domain knowledge model and the former may help modeling the learner's complex cognitive behavior, so as to be able to provide him or her with relevant feedback during a problem-solving process. To illustrate the application of the approach, we designed and developed several key components of an intelligent learning environment for teaching the concept of sacro-iliac screw fixation in orthopedic surgery, for which we videotaped and analyzed six surgical interventions in a French hospital. A preliminary gold-standard validation suggests that our diagnosis component is able to produce coherent diagnosis with acceptable response time.