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dc.contributor.authorKolekar, S.V.-
dc.contributor.authorSanjeevi, S.G-
dc.contributor.authorBormane, D.S.-
dc.date.accessioned2025-01-18T10:20:52Z-
dc.date.available2025-01-18T10:20:52Z-
dc.date.issued2011-01-
dc.identifier.citation10.1007/978-3-642-22194-1_80en_US
dc.identifier.urihttp://localhost:8080/xmlui/handle/123456789/2802-
dc.descriptionNITWen_US
dc.description.abstractWeb Usage Mining is a broad area of Web Mining which is associated with the Patterns extraction from logging information produced by web server. Web log mining is substantially the important part of Web Usage Mining (WUM) algorithm which involves transformation and interpretation of the logging information to predict the patterns as per different learning styles. Ultimately these patterns are useful to classify various defined profiles. To provide personalized learning environment to the user with respect to Adaptive User Interface, Web Usage Mining is very essential and useful step to implement. In this paper we build the module of E-learning architecture based on Web Usage Mining to assess the User’s behavior through web log analysis.en_US
dc.language.isoenen_US
dc.publisherSmart Innovation, Systems and Technologiesen_US
dc.subjectE-learningen_US
dc.subjectLog Mining Analysisen_US
dc.subjectAdaptive Learning stylesen_US
dc.subjectWeb Usage Miningen_US
dc.titleAcquisition of User’s Learning Styles Using Log Mining Analysis through Web Usage Mining Processen_US
dc.typeOtheren_US
Appears in Collections:Metallurgical and Materials Engineering

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