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Data pre-processing and data generation in the student flow case study

2020Conference object Open access
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dc.contributor.authorCavique, Luís
dc.contributor.authorPombinho, Paulo
dc.contributor.authorTallón Ballesteros, Antonio J.
dc.contributor.authorCorreia, Luís
dc.date.accessioned2020-11-18T17:32:21Z
dc.date.available2020-11-18T17:32:21Z
dc.date.issued2020
dc.description.abstractEducation covers a range of sectors from kindergarten to higher education. In the education system, each grade has three possible outcomes: dropout, retention and pass to the next grade. In this work, we study the data from the Department of Statistics of Education and Science (DGEEC) of the Education Ministry. DGEEC maintains those outcomes for each school year, therefore, this study seeks a longitudinal view based on student flow. The document reports the data pre-processing, a stochastic model based on the pre-processed data and a data generation process that uses the previous model.pt_PT
dc.description.sponsorshipThe authors would like to thank the FCT Projects of Scientific Research and Technological Development in Data Science and Artificial Intelligence in Public Administration, 2018-2022 (DSAIPA/DS/0039/2018), for its support. LCav, PP and LCor also acknowledge support by UID/MULTI/04046/2103 center grant from FCT, Portugal (to BioISI).pt_PT
dc.description.sponsorshipUID/MULTI/04046/2103
dc.description.versioninfo:eu-repo/semantics/publishedVersionpt_PT
dc.identifier.doi10.1007/978-3-030-62365-4_4pt_PT
dc.identifier.isbn978-3-030-62364-7(Print)
dc.identifier.isbn978-3-030-62365-4 (Online)
dc.identifier.urihttp://hdl.handle.net/10400.2/10184
dc.language.isoengpt_PT
dc.peerreviewedyespt_PT
dc.publisherLecture Notes in Computer Science - Springerpt_PT
dc.subjectData pre-processingpt_PT
dc.subjectData generationpt_PT
dc.subjectStudent flowpt_PT
dc.subjectStochastic modelpt_PT
dc.titleData pre-processing and data generation in the student flow case studypt_PT
dc.typeconference object
dspace.entity.typePublication
oaire.awardURIinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/DSAIPA%2FDS%2F0039%2F2018/PT
oaire.citation.endPage43pt_PT
oaire.citation.startPage35pt_PT
oaire.citation.titleIntelligent Data Engineering and Automated Learning – IDEAL 2020pt_PT
oaire.citation.volume12490pt_PT
oaire.fundingStream3599-PPCDT
person.familyNameCavique
person.familyNamePombalinho
person.familyNameTallón Ballesteros
person.familyNameCorreia
person.givenNameLuís
person.givenNamePaulo
person.givenNameAntonio Javier
person.givenNameLuís
person.identifierF-3440-2016
person.identifier.ciencia-id911E-84AC-3956
person.identifier.ciencia-idAF18-066F-60F6
person.identifier.ciencia-idCC18-5389-6CBA
person.identifier.orcid0000-0002-5590-1493
person.identifier.orcid0000-0001-7583-6791
person.identifier.orcid0000-0002-9699-1894
person.identifier.orcid0000-0003-2439-1168
person.identifier.ridM-3656-2013
person.identifier.scopus-author-id36192484400
person.identifier.scopus-author-id56865595100
project.funder.identifierhttp://doi.org/10.13039/501100001871
project.funder.nameFundação para a Ciência e a Tecnologia
rcaap.rightsopenAccesspt_PT
rcaap.typeconferenceObjectpt_PT
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