Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees

Descripción del Articulo

El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
Detalles Bibliográficos
Autores: Medina, Erik Cevallos, Chunga, Claudio Barahona, Armas-Aguirre, Jimmy, Grandon, Elizabeth E.
Formato: artículo
Fecha de Publicación:2020
Institución:Universidad Peruana de Ciencias Aplicadas
Repositorio:UPC-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorioacademico.upc.edu.pe:10757/656775
Enlace del recurso:http://hdl.handle.net/10757/656775
Nivel de acceso:acceso embargado
Materia:Bayesian Networks
Decision Trees
Educational Data Mining
predictive analysis
university dropout
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network_name_str UPC-Institucional
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dc.title.es_PE.fl_str_mv Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
title Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
spellingShingle Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
Medina, Erik Cevallos
Bayesian Networks
Decision Trees
Educational Data Mining
predictive analysis
university dropout
title_short Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
title_full Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
title_fullStr Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
title_full_unstemmed Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
title_sort Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
author Medina, Erik Cevallos
author_facet Medina, Erik Cevallos
Chunga, Claudio Barahona
Armas-Aguirre, Jimmy
Grandon, Elizabeth E.
author_role author
author2 Chunga, Claudio Barahona
Armas-Aguirre, Jimmy
Grandon, Elizabeth E.
author2_role author
author
author
dc.contributor.author.fl_str_mv Medina, Erik Cevallos
Chunga, Claudio Barahona
Armas-Aguirre, Jimmy
Grandon, Elizabeth E.
dc.subject.es_PE.fl_str_mv Bayesian Networks
Decision Trees
Educational Data Mining
predictive analysis
university dropout
topic Bayesian Networks
Decision Trees
Educational Data Mining
predictive analysis
university dropout
description El texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2021-07-19T15:18:48Z
dc.date.available.none.fl_str_mv 2021-07-19T15:18:48Z
dc.date.issued.fl_str_mv 2020-06-01
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.issn.none.fl_str_mv 21660727
dc.identifier.doi.none.fl_str_mv 10.23919/CISTI49556.2020.9141095
dc.identifier.uri.none.fl_str_mv http://hdl.handle.net/10757/656775
dc.identifier.eissn.none.fl_str_mv 21660735
dc.identifier.journal.es_PE.fl_str_mv Iberian Conference on Information Systems and Technologies, CISTI
dc.identifier.eid.none.fl_str_mv 2-s2.0-85089025364
dc.identifier.scopusid.none.fl_str_mv SCOPUS_ID:85089025364
dc.identifier.isni.none.fl_str_mv 0000 0001 2196 144X
identifier_str_mv 21660727
10.23919/CISTI49556.2020.9141095
21660735
Iberian Conference on Information Systems and Technologies, CISTI
2-s2.0-85089025364
SCOPUS_ID:85089025364
0000 0001 2196 144X
url http://hdl.handle.net/10757/656775
dc.language.iso.es_PE.fl_str_mv eng
language eng
dc.relation.url.es_PE.fl_str_mv https://ieeexplore.ieee.org/document/9141095
dc.rights.es_PE.fl_str_mv info:eu-repo/semantics/embargoedAccess
eu_rights_str_mv embargoedAccess
dc.format.es_PE.fl_str_mv application/html
dc.publisher.es_PE.fl_str_mv IEEE Computer Society
dc.source.es_PE.fl_str_mv Universidad Peruana de Ciencias Aplicadas (UPC)
Repositorio Académico - UPC
dc.source.none.fl_str_mv reponame:UPC-Institucional
instname:Universidad Peruana de Ciencias Aplicadas
instacron:UPC
instname_str Universidad Peruana de Ciencias Aplicadas
instacron_str UPC
institution UPC
reponame_str UPC-Institucional
collection UPC-Institucional
dc.source.journaltitle.none.fl_str_mv Iberian Conference on Information Systems and Technologies, CISTI
dc.source.volume.none.fl_str_mv 2020-June
bitstream.url.fl_str_mv https://repositorioacademico.upc.edu.pe/bitstream/10757/656775/1/license.txt
bitstream.checksum.fl_str_mv 8a4605be74aa9ea9d79846c1fba20a33
bitstream.checksumAlgorithm.fl_str_mv MD5
repository.name.fl_str_mv Repositorio académico upc
repository.mail.fl_str_mv upc@openrepository.com
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spelling c4d63eb1bd07913fe0cb76750b8e635b0364e03781bc8177658ef37e7e2353574832ce656228b995761b32f4527dfa58c1f7124c8eba41851479f8ee486c440b300Medina, Erik CevallosChunga, Claudio BarahonaArmas-Aguirre, JimmyGrandon, Elizabeth E.2021-07-19T15:18:48Z2021-07-19T15:18:48Z2020-06-012166072710.23919/CISTI49556.2020.9141095http://hdl.handle.net/10757/65677521660735Iberian Conference on Information Systems and Technologies, CISTI2-s2.0-85089025364SCOPUS_ID:850890253640000 0001 2196 144XEl texto completo de este trabajo no está disponible en el Repositorio Académico UPC por restricciones de la casa editorial donde ha sido publicado.This research proposes a prediction model that might help reducing the dropout rate of university students in Peru. For this, a three-phase predictive analysis model was designed which was combined with the stages proposed by the IBM SPSS Modeler methodology. Bayesian network techniques was compared with decision trees for their level of accuracy over other algorithms in an Educational Data Mining (EDM) scenario. Data were collected from 500 undergraduate students from a private university in Lima. The results indicate that Bayesian networks behave better than decision trees based on metrics of precision, accuracy, specificity, and error rate. Particularly, the accuracy of Bayesian networks reaches 67.10% while the accuracy for decision trees is 61.92% in the training sample for iteration with 8:2 rate. On the other hand, the variables athletic person (0.30%), own house (0.21%), and high school grades (0.13%) are the ones that contribute most to the prediction model for both Bayesian networks and decision trees.application/htmlengIEEE Computer Societyhttps://ieeexplore.ieee.org/document/9141095info:eu-repo/semantics/embargoedAccessUniversidad Peruana de Ciencias Aplicadas (UPC)Repositorio Académico - UPCIberian Conference on Information Systems and Technologies, CISTI2020-Junereponame:UPC-Institucionalinstname:Universidad Peruana de Ciencias Aplicadasinstacron:UPCBayesian NetworksDecision TreesEducational Data Miningpredictive analysisuniversity dropoutPredictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Treesinfo:eu-repo/semantics/articleLICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorioacademico.upc.edu.pe/bitstream/10757/656775/1/license.txt8a4605be74aa9ea9d79846c1fba20a33MD51false10757/656775oai:repositorioacademico.upc.edu.pe:10757/6567752021-07-19 15:18:49.526Repositorio académico upcupc@openrepository.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Predictive model to reduce the dropout rate of university students in Perú: Bayesian Networks vs. Decision Trees
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