Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Elements of information theory
Elements of information theory
Machine Learning
A tutorial on learning with Bayesian networks
Learning in graphical models
A Bayesian Diagnostic Algorithm for Student Modeling and its Evaluation
User Modeling and User-Adapted Interaction
Student Modeling from Conversational Test Data: A Bayesian Approach Without Priors
ITS '98 Proceedings of the 4th International Conference on Intelligent Tutoring Systems
Bayesian networks in educational testing
International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems - New trends in probabilistic graphical models
Data Mining
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Bayes nets in educational assessment: Where the numbers come from
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
International Journal of Artificial Intelligence in Education
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Using Bayesian networks as the representation language for student modeling has become a common practice. Many computer-assisted learning systems rely exclusively on human experts to provide information for constructing the network structures, however. We explore the possibility of applying mutual information-based heuristics and support vector machines to learn how students learn composite concepts, based on students' item responses to test items. The problem is challenging because it is well known that students' performances in taking tests do not reflect their competences faithfully. Experimental results indicate that the difficulty of identifying the true learning patterns varies with the degree of uncertainty in the relationship between students' performances in tests and their abilities in concepts. When the degree of uncertainty is moderate, it is possible to infer the unobservable learning patterns from students' external performances with computational techniques.