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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hibou, M. Labat, J.M. |
| Copyright Year | 2004 |
| Description | Author affiliation: AIDA/CRIP5 Univ. Rene Descartes Paris 5, France (Hibou, M.; Labat, J.M.) |
| Abstract | The modeling of the student cognitive state requires to take into account uncertainty, and during the past decade the use of Bayesian networks has grown as a method for dealing with such a problem. Many different ad-hoc models have been built in user modeling as well as in student modeling, using either expert knowledge elicitation or machine learning techniques but none of these methods is perfectly adapted to the case of student modeling. Moreover, the evolution of the student cognitive state only leads to probability update in these models, whereas we think that the topology of the network should also vary in order to reflect the changes in the student knowledge structure. We propose a general framework for embedding different Bayesian network student models in an architecture that handles transitions between them and dynamic adaptation to the learner. We aim at specifying and developing an application that could provide help to build such models without having to deal with the difficulties of using belief networks. |
| Sponsorship | Hewlett Packard Intel |
| Starting Page | 468 |
| Ending Page | 472 |
| File Size | 327966 |
| Page Count | 5 |
| File Format | |
| ISBN | 0780385969 |
| DOI | 10.1109/ITHET.2004.1358218 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2004-05-31 |
| Publisher Place | Turkey |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Intelligent networks Uncertainty Network topology Bayesian methods Machine learning Knowledge representation Cognition Random variables Time factors Intelligent systems |
| Content Type | Text |
| Resource Type | Article |
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