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Comparison of data mining techniques to identify signs of student desertion, based on academic performance

dc.contributor.authorPEREZ GUTIERREZ, BORIS RAINIERO
dc.date.accessioned2021-11-30T21:42:12Z
dc.date.available2021-11-30T21:42:12Z
dc.date.issued2020-01-04
dc.identifier.urihttp://repositorio.ufps.edu.co/handle/ufps/1592
dc.description.abstractUno de los grandes retos en las instituciones educativas consiste en poder establecer la posibilidad de retiro o deserción de sus estudiantes. En este artículo se presentan los resultados de un estudio de comparación de técnicas para apoyar la identificación de deserción estudiantil a partir del registro académico de los estudiantes de una Universidad en Colombia para el programa de Ingeniería de Sistemas. El registro académico se estableció para un periodo de 7 años. Árboles de decisión, regresión logística y Naive Bayes, fueron comparados para lograr establecer la mejor técnica de detección de desertores. Adicionalmente, la herramienta Watson Analytics de IBM fue utilizada para comparar su usabilidad y precisión para un usuario no experto. Nuestra experimentación demostró que el uso de algoritmos simples es suficiente para alcanzar niveles ideales de precisión. Estos resultados son presentados a la comunidad académica para ayudar en la disminución de la deserción estudiantil.spa
dc.description.abstractOne of the great challenges in educational institutions is to be able to establish the possibility of retirement or desertionof their students. This article presents the results of a comparative study of techniques to support the identificationof student dropouts using the academic record of students at a University in Colombia for the Systems Engineering program. The academic record was established for a period of 7 years. Decision trees, logistic regression, and Naive Bayes were compared to establish the best dropout detection technique. Additionally, IBM’s Watson Analytics tool was used to compare its usability and accuracy to a non-expert user. Our experience has shown that the use of simple algorithms is sufficient to achieve ideal levels of accuracy. These results are presented to the academic community to help decrease student dropout.eng
dc.format.extent12 páginasspa
dc.format.mimetypeapplication/pdfspa
dc.language.isospaspa
dc.publisherRevista UIS Ingenieríasspa
dc.relation.ispartofRevista UIS Ingenierías
dc.rightsEsta revista provee acceso libre inmediato a su contenido, bajo el principio de que hacer disponible gratuitamente la investigación al público permite un mayor intercambio de conocimiento global.eng
dc.sourcehttps://revistas.uis.edu.co/index.php/revistauisingenierias/article/view/9834spa
dc.titleComparación de técnicas de minería de datos para identificar indicios de deserción estudiantil, a partir del desempeño académicospa
dc.titleComparison of data mining techniques to identify signs of student desertion, based on academic performanceeng
dc.typeArtículo de revistaspa
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dc.identifier.doihttps://doi.org/10.18273/revuin.v19n1-2020018
dc.publisher.placeColombiaspa
dc.relation.citationeditionVol.19 No.1.(2020)spa
dc.relation.citationendpage204spa
dc.relation.citationissue1(2020)spa
dc.relation.citationstartpage193spa
dc.relation.citationvolume19spa
dc.relation.citesPerez-Gutierrez, B. R. (2020). Comparación de técnicas de minería de datos para identificar indicios de deserción estudiantil, a partir del desempeño académico. Revista UIS Ingenierías, 19(1), 193–204. https://doi.org/10.18273/revuin.v19n1-2020018
dc.relation.ispartofjournalRevista UIS Ingenieríasspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.rights.creativecommonsAtribución-SinDerivadas 4.0 Internacional (CC BY-ND 4.0)spa
dc.subject.proposaldatos estudiantilesspa
dc.subject.proposaleducación superiorspa
dc.subject.proposalminería de datosspa
dc.subject.proposalmodelos de predicciónspa
dc.subject.proposaldeserciónspa
dc.subject.proposalstudent dataeng
dc.subject.proposalhigher educationeng
dc.subject.proposaldata miningeng
dc.subject.proposalprediction modelseng
dc.subject.proposaldropouteng
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
oaire.accessrightshttp://purl.org/coar/access_right/c_abf2spa
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85spa
dc.type.versioninfo:eu-repo/semantics/publishedVersionspa


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