Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students

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—In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal a...

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Autores: Chamorro-Atalaya, Omar, Olivares-Zegarra, Soledad, Paredes-Soria, Alejandro, Samanamud-Loyola, Oscar, Anton-De los Santos, Marco, Anton-De los Santos, Juan, Fierro-Bravo, Maritte, Villanueva-Acosta, Victor
Formato: artículo
Fecha de Publicación:2021
Institución:Universidad Autónoma del Perú
Repositorio:AUTONOMA-Institucional
Lenguaje:inglés
OAI Identifier:oai:repositorio.autonoma.edu.pe:20.500.13067/1681
Enlace del recurso:https://hdl.handle.net/20.500.13067/1681
https://doi.org/10.14569/IJACSA.2021.0121289
Nivel de acceso:acceso abierto
Materia:Classification learner
Predictive system
Personal and social attitudes
Engineering students
https://purl.org/pe-repo/ocde/ford#2.02.04
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dc.title.es_PE.fl_str_mv Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
title Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
spellingShingle Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
Chamorro-Atalaya, Omar
Classification learner
Predictive system
Personal and social attitudes
Engineering students
https://purl.org/pe-repo/ocde/ford#2.02.04
title_short Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
title_full Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
title_fullStr Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
title_full_unstemmed Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
title_sort Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
author Chamorro-Atalaya, Omar
author_facet Chamorro-Atalaya, Omar
Olivares-Zegarra, Soledad
Paredes-Soria, Alejandro
Samanamud-Loyola, Oscar
Anton-De los Santos, Marco
Anton-De los Santos, Juan
Fierro-Bravo, Maritte
Villanueva-Acosta, Victor
author_role author
author2 Olivares-Zegarra, Soledad
Paredes-Soria, Alejandro
Samanamud-Loyola, Oscar
Anton-De los Santos, Marco
Anton-De los Santos, Juan
Fierro-Bravo, Maritte
Villanueva-Acosta, Victor
author2_role author
author
author
author
author
author
author
dc.contributor.author.fl_str_mv Chamorro-Atalaya, Omar
Olivares-Zegarra, Soledad
Paredes-Soria, Alejandro
Samanamud-Loyola, Oscar
Anton-De los Santos, Marco
Anton-De los Santos, Juan
Fierro-Bravo, Maritte
Villanueva-Acosta, Victor
dc.subject.es_PE.fl_str_mv Classification learner
Predictive system
Personal and social attitudes
Engineering students
topic Classification learner
Predictive system
Personal and social attitudes
Engineering students
https://purl.org/pe-repo/ocde/ford#2.02.04
dc.subject.ocde.es_PE.fl_str_mv https://purl.org/pe-repo/ocde/ford#2.02.04
description —In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal and social attitudes of university students of professional engineering careers. For this, the Machine Learning Classification Learner technique is used by means of the Matlab R2021a software. The results reflect a predictive system capable of classifying the four satisfaction classes (1: dissatisfied, 2: not very satisfied, 3: satisfied and 4: very satisfied) with an accuracy of 91.96%, a precision of 79.09%, a Sensitivity of 75.66% and a Specificity of 92.09%, regarding the students' perception of their personal and social attitudes. As a result, the higher institution will be able to take measures to monitor and correct the strengths and weaknesses of each variable related to satisfaction with the quality of the educational service.
publishDate 2021
dc.date.accessioned.none.fl_str_mv 2022-03-02T13:51:34Z
dc.date.available.none.fl_str_mv 2022-03-02T13:51:34Z
dc.date.issued.fl_str_mv 2021-12
dc.type.es_PE.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.citation.es_PE.fl_str_mv Chamorro-Atalaya, O., Olivares-Zegarra, S., Paredes-Soria, A., Samanamud-Loyola, O., Anton-De los Santos, M., Anton-De los Santos, J., Fierro-Bravo, M. & Villanueva-Acosta, V. (2021). “Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students” International Journal of Advanced Computer Science and Applications (IJACSA), 12(12), 718-725. http://dx.doi.org/10.14569/IJACSA.2021.0121289
dc.identifier.issn.none.fl_str_mv 2156-5570
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.13067/1681
dc.identifier.journal.es_PE.fl_str_mv International Journal of Advanced Computer Science and Applications (IJACSA)
dc.identifier.doi.none.fl_str_mv https://doi.org/10.14569/IJACSA.2021.0121289
identifier_str_mv Chamorro-Atalaya, O., Olivares-Zegarra, S., Paredes-Soria, A., Samanamud-Loyola, O., Anton-De los Santos, M., Anton-De los Santos, J., Fierro-Bravo, M. & Villanueva-Acosta, V. (2021). “Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students” International Journal of Advanced Computer Science and Applications (IJACSA), 12(12), 718-725. http://dx.doi.org/10.14569/IJACSA.2021.0121289
2156-5570
International Journal of Advanced Computer Science and Applications (IJACSA)
url https://hdl.handle.net/20.500.13067/1681
https://doi.org/10.14569/IJACSA.2021.0121289
dc.language.iso.es_PE.fl_str_mv eng
language eng
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dc.publisher.es_PE.fl_str_mv The Science and Information Organization
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dc.source.es_PE.fl_str_mv AUTONOMA
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dc.source.volume.es_PE.fl_str_mv 12
dc.source.issue.es_PE.fl_str_mv 12
dc.source.beginpage.es_PE.fl_str_mv 718
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spelling Chamorro-Atalaya, OmarOlivares-Zegarra, SoledadParedes-Soria, AlejandroSamanamud-Loyola, OscarAnton-De los Santos, MarcoAnton-De los Santos, JuanFierro-Bravo, MaritteVillanueva-Acosta, Victor2022-03-02T13:51:34Z2022-03-02T13:51:34Z2021-12Chamorro-Atalaya, O., Olivares-Zegarra, S., Paredes-Soria, A., Samanamud-Loyola, O., Anton-De los Santos, M., Anton-De los Santos, J., Fierro-Bravo, M. & Villanueva-Acosta, V. (2021). “Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students” International Journal of Advanced Computer Science and Applications (IJACSA), 12(12), 718-725. http://dx.doi.org/10.14569/IJACSA.2021.01212892156-5570https://hdl.handle.net/20.500.13067/1681International Journal of Advanced Computer Science and Applications (IJACSA)https://doi.org/10.14569/IJACSA.2021.0121289—In this competitive scenario of the educational system, higher education institutions use intelligent learning tools and techniques to predict the factors of student academic performance. Given this, the article aims to determine the supervised learning model for the predictive system of personal and social attitudes of university students of professional engineering careers. For this, the Machine Learning Classification Learner technique is used by means of the Matlab R2021a software. The results reflect a predictive system capable of classifying the four satisfaction classes (1: dissatisfied, 2: not very satisfied, 3: satisfied and 4: very satisfied) with an accuracy of 91.96%, a precision of 79.09%, a Sensitivity of 75.66% and a Specificity of 92.09%, regarding the students' perception of their personal and social attitudes. As a result, the higher institution will be able to take measures to monitor and correct the strengths and weaknesses of each variable related to satisfaction with the quality of the educational service.application/pdfengThe Science and Information OrganizationPEinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/AUTONOMA1212718725reponame:AUTONOMA-Institucionalinstname:Universidad Autónoma del Perúinstacron:AUTONOMAClassification learnerPredictive systemPersonal and social attitudesEngineering studentshttps://purl.org/pe-repo/ocde/ford#2.02.04Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Studentsinfo:eu-repo/semantics/articlehttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85122573471&doi=10.14569%2fIJACSA.2021.0121289&partnerID=40&md5ORIGINALSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdfSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdfArtículoapplication/pdf274862http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1681/1/Supervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf930e90da1eabbc1c4ac71dad0e7c5103MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-885http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1681/2/license.txt9243398ff393db1861c890baeaeee5f9MD52TEXTSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.txtSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.txtExtracted texttext/plain35376http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1681/3/Supervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.txtd4442698a8ab9ad827e92dca1397439dMD53THUMBNAILSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.jpgSupervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.jpgGenerated Thumbnailimage/jpeg7797http://repositorio.autonoma.edu.pe/bitstream/20.500.13067/1681/4/Supervised-Learning-Through-Classification-Learner-Techniques-For-The-Predictive-System-Of-Personal-And-Social-Attitudes.pdf.jpg8e217f47e38f558366860272bcd155f3MD5420.500.13067/1681oai:repositorio.autonoma.edu.pe:20.500.13067/16812022-03-03 03:00:23.642Repositorio de la Universidad Autonoma del Perúrepositorio@autonoma.pe
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Supervised Learning through Classification Learner Techniques for the Predictive System of Personal and Social Attitudes of Engineering Students
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