Combining unsupervised and supervised machine learning to build user models for intelligent learning environments
Traditional approaches to developing user models, especially for computer-based learning environments, are notoriously difficult and time-consuming because they rely heavily on expert-elicited knowledge about the target application and domain. Furthermore, because the expert-elicited knowledge us...
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| Language: | English |
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University of British Columbia
2011
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| Online Access: | http://hdl.handle.net/2429/31622 |