Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media
The data mining techniques allow for unveiling knowledge from large volumes of information, which have recently been explored in information analysis by educational institutions but already with an increasing demand for this sector to support decision-making. In this research, a methodology for comp...
Gespeichert in:
| Veröffentlicht in: | RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação 2020-08 (E32), p.455-468 |
|---|---|
| Hauptverfasser: | , , , , |
| Format: | Artikel |
| Sprache: | spa |
| Schlagworte: | |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| container_end_page | 468 |
|---|---|
| container_issue | E32 |
| container_start_page | 455 |
| container_title | RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação |
| container_volume | |
| creator | Chamorro-Sangoquiza, Diana C Vargas-Muñoz, Andrés M Umaquinga-Criollo, Ana C Becerra, Miguel A Peluffo-Ordóñez, Diego H |
| description | The data mining techniques allow for unveiling knowledge from large volumes of information, which have recently been explored in information analysis by educational institutions but already with an increasing demand for this sector to support decision-making. In this research, a methodology for comparing data mining techniques is proposed, which is to be applied to the analysis of academic patrons in students of media education. The experiments are carried out in a dataset of 285 instances and 36 attributes obtained from an educational survey applied to the students of the School of Education of the University of Barcelona 2017-2018. Keywords: academic performance patterns; feature selection; classifiers; multiple classifier; Matlab. 1.Introducción En la actualidad se vive en un mundo digitalizado, lo cual ha generado un explosivo crecimiento en el volumen de datos, que no siempre supone un aumento de conocimiento; como lo sostiene (Fayyad et al., 1996), y en varias áreas de la academia, ciencia y empresa (Alvarado-Pérez et al., 2015) procesar los datos con métodos clásicos resulta ser en muchos casos imposible, sumamente tedioso y con resultados superficiales e insatisfactorios. |
| format | Article |
| fullrecord | <record><control><sourceid>proquest</sourceid><recordid>TN_cdi_proquest_journals_2452331372</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2452331372</sourcerecordid><originalsourceid>FETCH-proquest_journals_24523313723</originalsourceid><addsrcrecordid>eNqNikEKwjAQRbNQUNQ7BFwL2rTarkXxAO5lSEaImEzNpEW8kQsXnqEXMxUP4DAw8_5_AzFerfP1oiqrYiRmzJdlmmKpqjIbi_uOY2MsSU2uhgDRtiQNytg9tbcauAdnPYbuBf1vIBLLXk3U4hVCghjI41c1yOhq7N4kQYPpns5qkujTckrBP0A6NBamYniGK-Psdydivt8dt4dFHejWIMfThZrgU3XK8iJTaqU2mfrP-gB1gFFD</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2452331372</pqid></control><display><type>article</type><title>Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media</title><source>EZB-FREE-00999 freely available EZB journals</source><creator>Chamorro-Sangoquiza, Diana C ; Vargas-Muñoz, Andrés M ; Umaquinga-Criollo, Ana C ; Becerra, Miguel A ; Peluffo-Ordóñez, Diego H</creator><creatorcontrib>Chamorro-Sangoquiza, Diana C ; Vargas-Muñoz, Andrés M ; Umaquinga-Criollo, Ana C ; Becerra, Miguel A ; Peluffo-Ordóñez, Diego H</creatorcontrib><description>The data mining techniques allow for unveiling knowledge from large volumes of information, which have recently been explored in information analysis by educational institutions but already with an increasing demand for this sector to support decision-making. In this research, a methodology for comparing data mining techniques is proposed, which is to be applied to the analysis of academic patrons in students of media education. The experiments are carried out in a dataset of 285 instances and 36 attributes obtained from an educational survey applied to the students of the School of Education of the University of Barcelona 2017-2018. Keywords: academic performance patterns; feature selection; classifiers; multiple classifier; Matlab. 1.Introducción En la actualidad se vive en un mundo digitalizado, lo cual ha generado un explosivo crecimiento en el volumen de datos, que no siempre supone un aumento de conocimiento; como lo sostiene (Fayyad et al., 1996), y en varias áreas de la academia, ciencia y empresa (Alvarado-Pérez et al., 2015) procesar los datos con métodos clásicos resulta ser en muchos casos imposible, sumamente tedioso y con resultados superficiales e insatisfactorios.</description><identifier>ISSN: 1646-9895</identifier><language>spa</language><publisher>Lousada: Associação Ibérica de Sistemas e Tecnologias de Informacao</publisher><subject>Classifiers ; Colleges & universities ; Comparative studies ; Data analysis ; Data mining ; Decision making ; Education ; Information management ; Machine learning ; Students</subject><ispartof>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação, 2020-08 (E32), p.455-468</ispartof><rights>2020. This work is published under https://creativecommons.org/licenses/by-nc-nd/4.0 (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780</link.rule.ids></links><search><creatorcontrib>Chamorro-Sangoquiza, Diana C</creatorcontrib><creatorcontrib>Vargas-Muñoz, Andrés M</creatorcontrib><creatorcontrib>Umaquinga-Criollo, Ana C</creatorcontrib><creatorcontrib>Becerra, Miguel A</creatorcontrib><creatorcontrib>Peluffo-Ordóñez, Diego H</creatorcontrib><title>Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media</title><title>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</title><description>The data mining techniques allow for unveiling knowledge from large volumes of information, which have recently been explored in information analysis by educational institutions but already with an increasing demand for this sector to support decision-making. In this research, a methodology for comparing data mining techniques is proposed, which is to be applied to the analysis of academic patrons in students of media education. The experiments are carried out in a dataset of 285 instances and 36 attributes obtained from an educational survey applied to the students of the School of Education of the University of Barcelona 2017-2018. Keywords: academic performance patterns; feature selection; classifiers; multiple classifier; Matlab. 1.Introducción En la actualidad se vive en un mundo digitalizado, lo cual ha generado un explosivo crecimiento en el volumen de datos, que no siempre supone un aumento de conocimiento; como lo sostiene (Fayyad et al., 1996), y en varias áreas de la academia, ciencia y empresa (Alvarado-Pérez et al., 2015) procesar los datos con métodos clásicos resulta ser en muchos casos imposible, sumamente tedioso y con resultados superficiales e insatisfactorios.</description><subject>Classifiers</subject><subject>Colleges & universities</subject><subject>Comparative studies</subject><subject>Data analysis</subject><subject>Data mining</subject><subject>Decision making</subject><subject>Education</subject><subject>Information management</subject><subject>Machine learning</subject><subject>Students</subject><issn>1646-9895</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNqNikEKwjAQRbNQUNQ7BFwL2rTarkXxAO5lSEaImEzNpEW8kQsXnqEXMxUP4DAw8_5_AzFerfP1oiqrYiRmzJdlmmKpqjIbi_uOY2MsSU2uhgDRtiQNytg9tbcauAdnPYbuBf1vIBLLXk3U4hVCghjI41c1yOhq7N4kQYPpns5qkujTckrBP0A6NBamYniGK-Psdydivt8dt4dFHejWIMfThZrgU3XK8iJTaqU2mfrP-gB1gFFD</recordid><startdate>20200801</startdate><enddate>20200801</enddate><creator>Chamorro-Sangoquiza, Diana C</creator><creator>Vargas-Muñoz, Andrés M</creator><creator>Umaquinga-Criollo, Ana C</creator><creator>Becerra, Miguel A</creator><creator>Peluffo-Ordóñez, Diego H</creator><general>Associação Ibérica de Sistemas e Tecnologias de Informacao</general><scope>3V.</scope><scope>7XB</scope><scope>8AL</scope><scope>8FE</scope><scope>8FG</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ARAPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BFMQW</scope><scope>BGLVJ</scope><scope>CCPQU</scope><scope>CLZPN</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>JQ2</scope><scope>K7-</scope><scope>M0N</scope><scope>P5Z</scope><scope>P62</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>Q9U</scope></search><sort><creationdate>20200801</creationdate><title>Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media</title><author>Chamorro-Sangoquiza, Diana C ; Vargas-Muñoz, Andrés M ; Umaquinga-Criollo, Ana C ; Becerra, Miguel A ; Peluffo-Ordóñez, Diego H</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-proquest_journals_24523313723</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>spa</language><creationdate>2020</creationdate><topic>Classifiers</topic><topic>Colleges & universities</topic><topic>Comparative studies</topic><topic>Data analysis</topic><topic>Data mining</topic><topic>Decision making</topic><topic>Education</topic><topic>Information management</topic><topic>Machine learning</topic><topic>Students</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Chamorro-Sangoquiza, Diana C</creatorcontrib><creatorcontrib>Vargas-Muñoz, Andrés M</creatorcontrib><creatorcontrib>Umaquinga-Criollo, Ana C</creatorcontrib><creatorcontrib>Becerra, Miguel A</creatorcontrib><creatorcontrib>Peluffo-Ordóñez, Diego H</creatorcontrib><collection>ProQuest Central (Corporate)</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Computing Database (Alumni Edition)</collection><collection>ProQuest SciTech Collection</collection><collection>ProQuest Technology Collection</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Advanced Technologies & Aerospace Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Continental Europe Database</collection><collection>Technology Collection</collection><collection>ProQuest One Community College</collection><collection>Latin America & Iberia Database</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>ProQuest Computer Science Collection</collection><collection>Computer Science Database</collection><collection>Computing Database</collection><collection>Advanced Technologies & Aerospace Database</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>ProQuest Central Basic</collection><jtitle>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Chamorro-Sangoquiza, Diana C</au><au>Vargas-Muñoz, Andrés M</au><au>Umaquinga-Criollo, Ana C</au><au>Becerra, Miguel A</au><au>Peluffo-Ordóñez, Diego H</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media</atitle><jtitle>RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação</jtitle><date>2020-08-01</date><risdate>2020</risdate><issue>E32</issue><spage>455</spage><epage>468</epage><pages>455-468</pages><issn>1646-9895</issn><abstract>The data mining techniques allow for unveiling knowledge from large volumes of information, which have recently been explored in information analysis by educational institutions but already with an increasing demand for this sector to support decision-making. In this research, a methodology for comparing data mining techniques is proposed, which is to be applied to the analysis of academic patrons in students of media education. The experiments are carried out in a dataset of 285 instances and 36 attributes obtained from an educational survey applied to the students of the School of Education of the University of Barcelona 2017-2018. Keywords: academic performance patterns; feature selection; classifiers; multiple classifier; Matlab. 1.Introducción En la actualidad se vive en un mundo digitalizado, lo cual ha generado un explosivo crecimiento en el volumen de datos, que no siempre supone un aumento de conocimiento; como lo sostiene (Fayyad et al., 1996), y en varias áreas de la academia, ciencia y empresa (Alvarado-Pérez et al., 2015) procesar los datos con métodos clásicos resulta ser en muchos casos imposible, sumamente tedioso y con resultados superficiales e insatisfactorios.</abstract><cop>Lousada</cop><pub>Associação Ibérica de Sistemas e Tecnologias de Informacao</pub><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1646-9895 |
| ispartof | RISTI : Revista Ibérica de Sistemas e Tecnologias de Informação, 2020-08 (E32), p.455-468 |
| issn | 1646-9895 |
| language | spa |
| recordid | cdi_proquest_journals_2452331372 |
| source | EZB-FREE-00999 freely available EZB journals |
| subjects | Classifiers Colleges & universities Comparative studies Data analysis Data mining Decision making Education Information management Machine learning Students |
| title | Estudio comparativo de técnicas de minería de datos para develar patrones de desempeño académico en enseñanza media |
| url | https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-26T10%3A48%3A41IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Estudio%20comparativo%20de%20t%C3%A9cnicas%20de%20miner%C3%ADa%20de%20datos%20para%20develar%20patrones%20de%20desempe%C3%B1o%20acad%C3%A9mico%20en%20ense%C3%B1anza%20media&rft.jtitle=RISTI%20:%20Revista%20Ib%C3%A9rica%20de%20Sistemas%20e%20Tecnologias%20de%20Informa%C3%A7%C3%A3o&rft.au=Chamorro-Sangoquiza,%20Diana%20C&rft.date=2020-08-01&rft.issue=E32&rft.spage=455&rft.epage=468&rft.pages=455-468&rft.issn=1646-9895&rft_id=info:doi/&rft_dat=%3Cproquest%3E2452331372%3C/proquest%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_pqid=2452331372&rft_id=info:pmid/&rfr_iscdi=true |