Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence
Objectives: This study presents the methodology for a model of multiple linear regression to assess the impact of the first partial grade and the percentage of non - attendance in the final grade students. Methods/Statistical Analysis: Descriptive Statistics and Inferential a program Industrial engi...
- Autores:
-
Viloria Silva, Amelec Jesus
Parody, Alexander
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2016
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/1150
- Acceso en línea:
- https://hdl.handle.net/11323/1150
https://repositorio.cuc.edu.co/
- Palabra clave:
- College dropout
Multiple linear regression
Prediction
- Rights
- openAccess
- License
- Atribución – No comercial – Compartir igual
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REDICUC - Repositorio CUC |
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|
dc.title.eng.fl_str_mv |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
title |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
spellingShingle |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence College dropout Multiple linear regression Prediction |
title_short |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
title_full |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
title_fullStr |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
title_full_unstemmed |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
title_sort |
Methodology for obtaining a predictive model academic performance of students from first partial note and percentage of absence |
dc.creator.fl_str_mv |
Viloria Silva, Amelec Jesus Parody, Alexander |
dc.contributor.author.spa.fl_str_mv |
Viloria Silva, Amelec Jesus Parody, Alexander |
dc.subject.eng.fl_str_mv |
College dropout Multiple linear regression Prediction |
topic |
College dropout Multiple linear regression Prediction |
description |
Objectives: This study presents the methodology for a model of multiple linear regression to assess the impact of the first partial grade and the percentage of non - attendance in the final grade students. Methods/Statistical Analysis: Descriptive Statistics and Inferential a program Industrial engineering a university in Colombia. Findings: After the generation and validation of the model was obtained that it explains 83.38% of the variability of the final grade students analyzed (134 students), and this significantly high percentage as a tool to determine the outcome of a student and generate recovery strategies those with a very low projection in its final note, it should be noted that the model was rigorously validated statistically. Application/Improvements: This methodology is proposed as a model for similar studies in other institutions. |
publishDate |
2016 |
dc.date.issued.none.fl_str_mv |
2016 |
dc.date.accessioned.none.fl_str_mv |
2018-11-16T20:30:25Z |
dc.date.available.none.fl_str_mv |
2018-11-16T20:30:25Z |
dc.type.spa.fl_str_mv |
Artículo de revista |
dc.type.coar.fl_str_mv |
http://purl.org/coar/resource_type/c_2df8fbb1 |
dc.type.coar.spa.fl_str_mv |
http://purl.org/coar/resource_type/c_6501 |
dc.type.content.spa.fl_str_mv |
Text |
dc.type.driver.spa.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.redcol.spa.fl_str_mv |
http://purl.org/redcol/resource_type/ART |
dc.type.version.spa.fl_str_mv |
info:eu-repo/semantics/acceptedVersion |
format |
http://purl.org/coar/resource_type/c_6501 |
status_str |
acceptedVersion |
dc.identifier.issn.spa.fl_str_mv |
09746846 |
dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/1150 |
dc.identifier.instname.spa.fl_str_mv |
Corporación Universidad de la Costa |
dc.identifier.reponame.spa.fl_str_mv |
REDICUC - Repositorio CUC |
dc.identifier.repourl.spa.fl_str_mv |
https://repositorio.cuc.edu.co/ |
identifier_str_mv |
09746846 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
url |
https://hdl.handle.net/11323/1150 https://repositorio.cuc.edu.co/ |
dc.language.iso.none.fl_str_mv |
eng |
language |
eng |
dc.rights.spa.fl_str_mv |
Atribución – No comercial – Compartir igual |
dc.rights.accessrights.spa.fl_str_mv |
info:eu-repo/semantics/openAccess |
dc.rights.coar.spa.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
rights_invalid_str_mv |
Atribución – No comercial – Compartir igual http://purl.org/coar/access_right/c_abf2 |
eu_rights_str_mv |
openAccess |
dc.publisher.spa.fl_str_mv |
Indian Journal of Science and Technology |
institution |
Corporación Universidad de la Costa |
bitstream.url.fl_str_mv |
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repository.name.fl_str_mv |
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repdigital@cuc.edu.co |
_version_ |
1811760804192583680 |
spelling |
Viloria Silva, Amelec JesusParody, Alexander2018-11-16T20:30:25Z2018-11-16T20:30:25Z201609746846https://hdl.handle.net/11323/1150Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Objectives: This study presents the methodology for a model of multiple linear regression to assess the impact of the first partial grade and the percentage of non - attendance in the final grade students. Methods/Statistical Analysis: Descriptive Statistics and Inferential a program Industrial engineering a university in Colombia. Findings: After the generation and validation of the model was obtained that it explains 83.38% of the variability of the final grade students analyzed (134 students), and this significantly high percentage as a tool to determine the outcome of a student and generate recovery strategies those with a very low projection in its final note, it should be noted that the model was rigorously validated statistically. Application/Improvements: This methodology is proposed as a model for similar studies in other institutions.Viloria Silva, Amelec Jesus-0000-0003-2673-6350-600Parody, Alexander-d4c60c43-64a2-466b-8114-ad27a6900a33-0engIndian Journal of Science and TechnologyAtribución – No comercial – Compartir igualinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2College dropoutMultiple linear regressionPredictionMethodology for obtaining a predictive model academic performance of students from first partial note and percentage of absenceArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALMethodology for Obtaining a Predictive.pdfMethodology for Obtaining a Predictive.pdfapplication/pdf242368https://repositorio.cuc.edu.co/bitstreams/a173b2cf-12d3-49c7-8837-9b7e8b59a952/download905dcc2501c25c6a4fdc858d549029ffMD51LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/043bb80e-519e-4aa9-a2f2-56085f5fc3cf/download8a4605be74aa9ea9d79846c1fba20a33MD52THUMBNAILMethodology for Obtaining a Predictive.pdf.jpgMethodology for Obtaining a Predictive.pdf.jpgimage/jpeg58911https://repositorio.cuc.edu.co/bitstreams/12e79b35-2960-40cd-9551-ad18c54905cd/downloadd79730ea22c0ea5f13745877f66e6a93MD54TEXTMethodology for Obtaining a Predictive.pdf.txtMethodology for Obtaining a Predictive.pdf.txttext/plain17528https://repositorio.cuc.edu.co/bitstreams/65dd58b4-4430-4e95-962b-0b72151feff8/download9dd53a0aa6b590841afbe9d586e38e38MD5511323/1150oai:repositorio.cuc.edu.co:11323/11502024-09-17 12:46:40.438open.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |