dc.contributorRigo, Sandro José
dc.creatorCambruzzi, Wagner Luiz
dc.date.accessioned2015-07-28T20:32:18Z
dc.date.accessioned2022-09-09T21:32:20Z
dc.date.accessioned2023-03-13T19:11:56Z
dc.date.available2015-07-28T20:32:18Z
dc.date.available2022-09-09T21:32:20Z
dc.date.available2023-03-13T19:11:56Z
dc.date.created2015-07-28T20:32:18Z
dc.date.created2022-09-09T21:32:20Z
dc.date.issued2014-04-15
dc.identifierhttp://148.201.128.228:8080/xmlui/handle/20.500.12032/32884
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6149223
dc.description.abstractEducational Data mining (EDM) and Learning Analytics (LA) applications have been adopted in mitigation of dropout, providing information about students who are employed in pedagogical interventions. The most papers about the implementation of these systems describe the techniques employed, there are few evaluations of their large-scale use, apart from the lack of detail about the causes of dropout. This work presents a study of factors involved in dropout and describes the use of a system for EDM and LA during 18 months for undergraduate courses in distance education. The analysis of the factors traditionally monitored and used in EDM and LA systems is extended, with the inclusion of elements associated with the role exercised by the teachers and by institutional methodological aspects. The system has flexibility in integration and use of data generated in the process of digital mediation, which allows different support tools to be available. Some results are the identification of evaders students profiles and the realization of pedagogical actions with reducing evasion.
dc.publisherUniversidade do Vale do Rio dos Sinos
dc.rightsopenAccess
dc.subjectEducação - processamento de dados
dc.subjectLearning analytics
dc.titleGVwise: uma aplicação de learning analytics para a redução da evasão na educação à distância
dc.typeDissertação


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