Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in expert systems: theory and algorithms
Probabilistic reasoning in expert systems: theory and algorithms
Conceptual and Meta Learning During Coached Problem Solving
ITS '96 Proceedings of the Third International Conference on Intelligent Tutoring Systems
Adaptive Bayesian Networks for Multilevel Student Modelling
ITS '00 Proceedings of the 5th International Conference on Intelligent Tutoring Systems
The Knowledge Engineering Review
Bayesian networks for student model engineering
Computers & Education
Layered evaluation of interactive adaptive systems: framework and formative methods
User Modeling and User-Adapted Interaction
Performance comparison of item-to-item skills models with the IRT single latent trait model
UMAP'11 Proceedings of the 19th international conference on User modeling, adaption, and personalization
Learning students' learning patterns with support vector machines
ISMIS'06 Proceedings of the 16th international conference on Foundations of Intelligent Systems
Bayesian student models based on item to item knowledge structures
EC-TEL'06 Proceedings of the First European conference on Technology Enhanced Learning: innovative Approaches for Learning and Knowledge Sharing
Experimental analysis of the q-matrix method in knowledge discovery
ISMIS'05 Proceedings of the 15th international conference on Foundations of Intelligent Systems
Student modeling with atomic bayesian networks
ITS'06 Proceedings of the 8th international conference on Intelligent Tutoring Systems
A review of recent advances in learner and skill modeling in intelligent learning environments
User Modeling and User-Adapted Interaction
Toward the application of argumentation to interactive learning systems
ArgMAS'11 Proceedings of the 8th international conference on Argumentation in Multi-Agent Systems
| Hi-index | 0.00 |
Although conventional tests are often used for determining a student's overall competence, they are seldom used for determining a finegrained model. However, this problem does arise occasionally, such as when a conventional test is used to initialize the student model of an ITS. Existing psychometric techniques for solving this problem are intractable. Straightforward Bayesian techniques are also inapplicable because they depend too strongly on the priors, which are often not available. Our solution is to base the assessment on the difference between the prior and posterior probabilities. If the test data raise the posterior probability of mastery of a piece of knowledge even slightly above its prior probability, then that is interpreted as evidence that the student has mastered that piece of knowledge. Evaluation of this technique with artificial students indicates that it can deliver highly accurate assessments.