Guidelines for the Application of Data Mining to the Problem of School Dropout

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dc.contributor.authorde Carvalho, Veronica Oliveira [UNESP]
dc.contributor.authorPenteado, Bruno Elias
dc.contributor.authorde Sousa, Leandro Rondado [UNESP]
dc.contributor.authorAffonso, Frank José [UNESP]
dc.contributor.institutionUniversidade Estadual Paulista (UNESP)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2023-03-02T11:51:30Z
dc.date.available2023-03-02T11:51:30Z
dc.date.issued2022-01-01
dc.description.abstractDropout is a complex phenomenon based on interrelated factors such as personal, institutional, structural, sociocultural, among other ones. It represents a waste of resources for students, their families, schools and society, and continues to be a challenge for educational institutions. In the last decade, the growing amount of data from educational institutions and the emergence of data science have led to data mining methodologies to explore this problem empirically. In this work, we map the literature on how data mining has been addressed face-to-face dropout. We synthesize different aspects, all of them related to steps of a generic data mining process. Our findings reveal a low level of formalism in theories, methodologies and pre-processing steps, with most papers making comparisons of different algorithms and features on the data available in the institution’s information system. Finally, we present some guidelines that can be used to improve the research on this topic.en
dc.description.affiliationInstituto de Geociências e Ciências Exatas Universidade Estadual Paulista (Unesp)
dc.description.affiliationInstituto de Ciências Matemáticas e de Computação Universidade de São Paulo (USP)
dc.description.affiliationUnespInstituto de Geociências e Ciências Exatas Universidade Estadual Paulista (Unesp)
dc.format.extent55-72
dc.identifierhttp://dx.doi.org/10.1007/978-3-031-14756-2_4
dc.identifier.citationCommunications in Computer and Information Science, v. 1624 CCIS, p. 55-72.
dc.identifier.doi10.1007/978-3-031-14756-2_4
dc.identifier.issn1865-0937
dc.identifier.issn1865-0929
dc.identifier.scopus2-s2.0-85136916311
dc.identifier.urihttp://hdl.handle.net/11449/242213
dc.language.isoeng
dc.relation.ispartofCommunications in Computer and Information Science
dc.sourceScopus
dc.subjectData mining
dc.subjectRevisited
dc.subjectSchool dropout
dc.subjectSystematic Literature Mapping
dc.titleGuidelines for the Application of Data Mining to the Problem of School Dropouten
dc.typeTrabalho apresentado em evento

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE