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

Detalhes bibliográficos
Autor(a) principal: de Carvalho, Veronica Oliveira [UNESP]
Data de Publicação: 2022
Outros Autores: Penteado, Bruno Elias, de Sousa, Leandro Rondado [UNESP], Affonso, Frank José [UNESP]
Tipo de documento: Artigo de conferência
Idioma: eng
Título da fonte: Repositório Institucional da UNESP
Texto Completo: http://dx.doi.org/10.1007/978-3-031-14756-2_4
http://hdl.handle.net/11449/242213
Resumo: Dropout 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.
id UNSP_c777202148ef8df887a4655f26782fe4
oai_identifier_str oai:repositorio.unesp.br:11449/242213
network_acronym_str UNSP
network_name_str Repositório Institucional da UNESP
repository_id_str 2946
spelling Guidelines for the Application of Data Mining to the Problem of School DropoutData miningRevisitedSchool dropoutSystematic Literature MappingDropout 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.Instituto de Geociências e Ciências Exatas Universidade Estadual Paulista (Unesp)Instituto de Ciências Matemáticas e de Computação Universidade de São Paulo (USP)Instituto de Geociências e Ciências Exatas Universidade Estadual Paulista (Unesp)Universidade Estadual Paulista (UNESP)Universidade de São Paulo (USP)de Carvalho, Veronica Oliveira [UNESP]Penteado, Bruno Eliasde Sousa, Leandro Rondado [UNESP]Affonso, Frank José [UNESP]2023-03-02T11:51:30Z2023-03-02T11:51:30Z2022-01-01info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/conferenceObject55-72http://dx.doi.org/10.1007/978-3-031-14756-2_4Communications in Computer and Information Science, v. 1624 CCIS, p. 55-72.1865-09371865-0929http://hdl.handle.net/11449/24221310.1007/978-3-031-14756-2_42-s2.0-85136916311Scopusreponame:Repositório Institucional da UNESPinstname:Universidade Estadual Paulista (UNESP)instacron:UNESPengCommunications in Computer and Information Scienceinfo:eu-repo/semantics/openAccess2023-03-02T11:51:30Zoai:repositorio.unesp.br:11449/242213Repositório InstitucionalPUBhttp://repositorio.unesp.br/oai/requestopendoar:29462024-08-05T21:52:00.018683Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)false
dc.title.none.fl_str_mv Guidelines for the Application of Data Mining to the Problem of School Dropout
title Guidelines for the Application of Data Mining to the Problem of School Dropout
spellingShingle Guidelines for the Application of Data Mining to the Problem of School Dropout
de Carvalho, Veronica Oliveira [UNESP]
Data mining
Revisited
School dropout
Systematic Literature Mapping
title_short Guidelines for the Application of Data Mining to the Problem of School Dropout
title_full Guidelines for the Application of Data Mining to the Problem of School Dropout
title_fullStr Guidelines for the Application of Data Mining to the Problem of School Dropout
title_full_unstemmed Guidelines for the Application of Data Mining to the Problem of School Dropout
title_sort Guidelines for the Application of Data Mining to the Problem of School Dropout
author de Carvalho, Veronica Oliveira [UNESP]
author_facet de Carvalho, Veronica Oliveira [UNESP]
Penteado, Bruno Elias
de Sousa, Leandro Rondado [UNESP]
Affonso, Frank José [UNESP]
author_role author
author2 Penteado, Bruno Elias
de Sousa, Leandro Rondado [UNESP]
Affonso, Frank José [UNESP]
author2_role author
author
author
dc.contributor.none.fl_str_mv Universidade Estadual Paulista (UNESP)
Universidade de São Paulo (USP)
dc.contributor.author.fl_str_mv de Carvalho, Veronica Oliveira [UNESP]
Penteado, Bruno Elias
de Sousa, Leandro Rondado [UNESP]
Affonso, Frank José [UNESP]
dc.subject.por.fl_str_mv Data mining
Revisited
School dropout
Systematic Literature Mapping
topic Data mining
Revisited
School dropout
Systematic Literature Mapping
description Dropout 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.
publishDate 2022
dc.date.none.fl_str_mv 2022-01-01
2023-03-02T11:51:30Z
2023-03-02T11:51:30Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/conferenceObject
format conferenceObject
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://dx.doi.org/10.1007/978-3-031-14756-2_4
Communications in Computer and Information Science, v. 1624 CCIS, p. 55-72.
1865-0937
1865-0929
http://hdl.handle.net/11449/242213
10.1007/978-3-031-14756-2_4
2-s2.0-85136916311
url http://dx.doi.org/10.1007/978-3-031-14756-2_4
http://hdl.handle.net/11449/242213
identifier_str_mv Communications in Computer and Information Science, v. 1624 CCIS, p. 55-72.
1865-0937
1865-0929
10.1007/978-3-031-14756-2_4
2-s2.0-85136916311
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv Communications in Computer and Information Science
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv 55-72
dc.source.none.fl_str_mv Scopus
reponame:Repositório Institucional da UNESP
instname:Universidade Estadual Paulista (UNESP)
instacron:UNESP
instname_str Universidade Estadual Paulista (UNESP)
instacron_str UNESP
institution UNESP
reponame_str Repositório Institucional da UNESP
collection Repositório Institucional da UNESP
repository.name.fl_str_mv Repositório Institucional da UNESP - Universidade Estadual Paulista (UNESP)
repository.mail.fl_str_mv
_version_ 1808129367481516032