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dc.contributor.authorCarmona, C. J.en
dc.contributor.authorElizondo, Daviden
dc.date.accessioned2017-06-06T13:19:07Z
dc.date.available2017-06-06T13:19:07Z
dc.date.issued2012-09-01
dc.identifier.citationCarmona, C. J. and Elizondo, D. (2012) Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems.en
dc.identifier.urihttp://hdl.handle.net/2086/14229
dc.description.abstractNowadays, there is a increasing in the use of learning management systems from the universities. This type of systems are also known under other di erent terms as course management systems or learning content management systems. Speci cally, these systems are e-learning platforms o ering di erent facilities for information sharing and communication between the participants in the e-learning process. This contribution presents an experimental study with several subgroup discovery algorithms based on evolutionary fuzzy systems using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best performing algorithm, NMEEF-SD, are also presented. The most representative results obtained by this algorithm are summarised in order to obtain knowledge that can allow teachers to take actions to improve student performance.en
dc.publisherDMUen
dc.subjectLearning Systemsen
dc.subjecte-learningen
dc.subjectevolutionary fuzzy systemsen
dc.titleAnalysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systemsen
dc.typeTechnical Reporten
dc.researchgroupDIGITSen
dc.peerreviewedNoen
dc.funderN/Aen
dc.projectidN/Aen
dc.cclicenceCC-BY-NCen
dc.date.acceptance2012-09-01en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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