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dc.contributor.authorXhafa Xhafa, Fatos
dc.contributor.authorCaballé Llobet, Santiago
dc.contributor.authorBarolli, Leonard
dc.contributor.authorMolina, Albert
dc.contributor.authorMiho, Rozeta
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Ciències de la Computació
dc.identifier.citationXhafa, F., Caballé, S., Barolli, L., Molina, A., Miho, R. Using bi-clustering algorithm for analyzing online users activity in a virtual campus. A: International Conference on Intelligent Networking and Collaborative Systems. "Second International Conference on Intelligent Networking and Collaborative Systems, Thessaloniki, Greece, 24–26 November 2010: proceedings". Institute of Electrical and Electronics Engineers (IEEE), 2010, p. 214-221.
dc.description.abstractData mining algorithms have been proved to be useful for the processing of large data sets in order to extract relevant information and knowledge. Such algorithms are also important for analyzing data collected from the users' activity users. One family of such data analysis is that of mining of log files of online applications that register the actions of online users during long periods of time. A relevant objective in this case is to study the behavior of online users and feedback the design processes of online applications to provide better usability and adaption to users' preferences. The context of this work is that of a virtual campus in which thousands of students and tutors carry out the learning and teaching activity using online applications. The information stored in log files of virtual campuses tend to be large, complex and heterogeneous in nature. Hence, their mining requires both efficient and intelligent processing and analysis of user interaction data during long-term learning activities. In this paper, we present a bi-clustering algorithm for processing large log data sets from the online daily activity of students in a real virtual campus. Our approach is useful to extract relevant knowledge about user activity such as navigation patterns, activities performed as well as to study time parameters related to such activities. The extracted information can be useful not only to students and tutors to stimulate and improve their experience when interacting with the system but also to the designers and developers of the virtual campus in order to better support the online teaching and learning.
dc.format.extent8 p.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.subjectÀrees temàtiques de la UPC::Ensenyament i aprenentatge::TIC's aplicades a l'educació
dc.subjectÀrees temàtiques de la UPC::Informàtica::Sistemes d'informació
dc.subject.lcshData mining
dc.subject.lcshWeb-based instruction
dc.subject.lcshComputer-assisted instruction
dc.subject.otherBi-clustering algorithm
dc.subject.otherMining techniques
dc.subject.otherOnline users
dc.subject.otherUser modelling
dc.subject.otherVirtual campus
dc.titleUsing bi-clustering algorithm for analyzing online users activity in a virtual campus
dc.typeConference report
dc.subject.lemacMineria de dades
dc.subject.lemacEnsenyament virtual
dc.subject.lemacEnsenyament assistit per ordinador
dc.description.peerreviewedPeer Reviewed
dc.rights.accessOpen Access
dc.description.versionPostprint (published version)
upcommons.citation.authorXhafa, F.; Caballé, S.; Barolli, L.; Molina, A.; Miho, R.
upcommons.citation.contributorInternational Conference on Intelligent Networking and Collaborative Systems
upcommons.citation.publicationNameSecond International Conference on Intelligent Networking and Collaborative Systems, Thessaloniki, Greece, 24–26 November 2010: proceedings

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