Please use this identifier to cite or link to this item: http://hdl.handle.net/11328/3796
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dc.contributor.authorOliveira, Catarina Félix de-
dc.contributor.authorSobral, Sónia Rolland-
dc.contributor.authorFerreira, Maria João-
dc.contributor.authorMoreira, Fernando-
dc.date.accessioned2021-11-05T15:42:00Z-
dc.date.available2021-11-05T15:42:00Z-
dc.date.issued2021-11-04-
dc.identifier.citationOliveira, C. F., Sobral, S. R., Ferreira, M. J., & Moreira, F. (2021). How Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Review. Big Data and Cognitive Computing, 2021, 5(4), 64. https://doi.org/10.3390/bdcc5040064. Disponível no Repositório UPT, http://hdl.handle.net/11328/3796pt_PT
dc.identifier.issn2504-2289-
dc.identifier.urihttp://hdl.handle.net/11328/3796-
dc.description.abstractRetention and dropout of higher education students is a subject that must be analysed carefully. Learning analytics can be used to help prevent failure cases. The purpose of this paper is to analyse the scientific production in this area in higher education in journals indexed in Clarivate Analytics’ Web of Science and Elsevier’s Scopus. We use a bibliometric and systematic study to obtain deep knowledge of the referred scientific production. The information gathered allows us to perceive where, how, and in what ways learning analytics has been used in the latest years. By analysing studies performed all over the world, we identify what kinds of data and techniques are used to approach the subject. We propose a feature classification into several categories and subcategories, regarding student and external features. Student features can be seen as personal or academic data, while external factors include information about the university, environment, and support offered to the students. To approach the problems, authors successfully use data mining applied to the identified educational data. We also identify some other concerns, such as privacy issues, that need to be considered in the studies.pt_PT
dc.language.isoengpt_PT
dc.publisherMDPI - Multidisciplinary Digital Publishing Institutept_PT
dc.relation.ispartofseries;4-
dc.rightsopenAccesspt_PT
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt_PT
dc.subjectLearning analyticspt_PT
dc.subjectEducational data miningpt_PT
dc.subjectHigher educationpt_PT
dc.subjectDropoutpt_PT
dc.subjectRetentionpt_PT
dc.titleHow Does Learning Analytics Contribute to Prevent Students’ Dropout in Higher Education: A Systematic Literature Reviewpt_PT
dc.typearticlept_PT
dc.peerreviewedyespt_PT
degois.publication.firstPage64pt_PT
degois.publication.volume5pt_PT
degois.publication.titleBig Data and Cognitive Computingpt_PT
dc.identifier.doihttps://doi.org/10.3390/bdcc5040064pt_PT
Appears in Collections:REMIT – Artigos em Revistas Internacionais / Papers in International Journals

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