Integration of data technology for analyzing university dropout
Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the dat...
- Autores:
-
amelec, viloria
Garcia Padilla, Jholman
Vargas Mercado, Carlos
Hernández Palma, Hugo
ORELLANO LLINAS, NATALY
ARRAZOLA DAVID, MONICA JUDITH
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5878
- Acceso en línea:
- https://hdl.handle.net/11323/5878
https://repositorio.cuc.edu.co/
- Palabra clave:
- University retention
University dropout
Data mining
Education
Engineering
Big Data
- Rights
- openAccess
- License
- CC0 1.0 Universal
| id |
RCUC2_99511d812abb369b178a3c933c42cc54 |
|---|---|
| oai_identifier_str |
oai:repositorio.cuc.edu.co:11323/5878 |
| network_acronym_str |
RCUC2 |
| network_name_str |
REDICUC - Repositorio CUC |
| repository_id_str |
|
| dc.title.spa.fl_str_mv |
Integration of data technology for analyzing university dropout |
| title |
Integration of data technology for analyzing university dropout |
| spellingShingle |
Integration of data technology for analyzing university dropout University retention University dropout Data mining Education Engineering Big Data |
| title_short |
Integration of data technology for analyzing university dropout |
| title_full |
Integration of data technology for analyzing university dropout |
| title_fullStr |
Integration of data technology for analyzing university dropout |
| title_full_unstemmed |
Integration of data technology for analyzing university dropout |
| title_sort |
Integration of data technology for analyzing university dropout |
| dc.creator.fl_str_mv |
amelec, viloria Garcia Padilla, Jholman Vargas Mercado, Carlos Hernández Palma, Hugo ORELLANO LLINAS, NATALY ARRAZOLA DAVID, MONICA JUDITH |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Garcia Padilla, Jholman Vargas Mercado, Carlos Hernández Palma, Hugo ORELLANO LLINAS, NATALY ARRAZOLA DAVID, MONICA JUDITH |
| dc.subject.spa.fl_str_mv |
University retention University dropout Data mining Education Engineering Big Data |
| topic |
University retention University dropout Data mining Education Engineering Big Data |
| description |
Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the data provided by the Engineering departments of the University of Mumbai, in India, determine that the variables that best explain a student's dropout are the socioeconomic factors and the income score provided by the University Admission Test (UAT). According to the decision tree technique, it is concluded that the retention is 78.3%. The quality of the classifiers allows to ensure that their predictions are correct, with statistical levels of ROC curve are 76%, 75%, and 83% successful for Bayesian network classifiers, decision tree, and neural network respectively. |
| publishDate |
2019 |
| dc.date.issued.none.fl_str_mv |
2019-08-19 |
| dc.date.accessioned.none.fl_str_mv |
2020-01-20T15:09:59Z |
| dc.date.available.none.fl_str_mv |
2020-01-20T15:09:59Z |
| 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 |
0000-2010 |
| dc.identifier.uri.spa.fl_str_mv |
https://hdl.handle.net/11323/5878 |
| 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 |
0000-2010 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
| url |
https://hdl.handle.net/11323/5878 https://repositorio.cuc.edu.co/ |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.spa.fl_str_mv |
https://doi.org/10.1016/j.procs.2019.08.079 |
| dc.relation.references.spa.fl_str_mv |
[1] Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017). [2] Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimiento 6(1), 1–17 (2009). [3] Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018). [4] Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching – Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943,1-12 (2018). [5] Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística, vol. 9(1), 93-106 (2016). [6] Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Designing Data Warehouses: From Business Requirement Analysis to Multidimensional Modeling. In Proceedings of the 1st Int. Workshop on Requirements Engineering for Business Need and IT Alignment. Paris, France (2005). [7] Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018). [8] Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999). [9] Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996). [10] Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. European Journal of Operational Research 237(1), 1095-104 (2014). [11] Sekmen, F., Kurkcu, M.: An Early Warning System for Turkey: The Forecasting of Economic Crisis by Using the Artificial Neural Networks. Asian Economic and Financial Review 4(1), 529-43 (2014). [12] Singhal, D., Swarup, K.S.: Electricity price forecasting using artificial neural networks. IJEPE 33 (1), 550-55 (2011). [13] Mombeini, H., Yazdani-Chamzini, A.: Modelling Gold Price via Artificial Neural Network. Journal of Economics, Business and Management 3 (7), 699-703 (2015). [14] Kulkarni, S., Haidar, I.: Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Future Prices. International Journal of Computer Science and Information Security 2 (1), 81-89 (2009). [15] Bontempi, G., Ben Taieb, S., Borgne, Y. A.: Machine learning strategies for time series forecasting. In Lecture Notes in Business Information Processing, ed M.-A. Aufaure., and E. Zimányi, Heidelberg: Springer 138 (1), 70-73 (2013). |
| dc.rights.spa.fl_str_mv |
CC0 1.0 Universal |
| dc.rights.uri.spa.fl_str_mv |
http://creativecommons.org/publicdomain/zero/1.0/ |
| 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 |
CC0 1.0 Universal http://creativecommons.org/publicdomain/zero/1.0/ http://purl.org/coar/access_right/c_abf2 |
| eu_rights_str_mv |
openAccess |
| dc.publisher.spa.fl_str_mv |
Procedia Computer Science |
| institution |
Corporación Universidad de la Costa |
| bitstream.url.fl_str_mv |
https://repositorio.cuc.edu.co/bitstreams/ac6e5445-57a0-4155-ad27-42601403759a/download https://repositorio.cuc.edu.co/bitstreams/d0db1051-6b36-4e53-852e-3dc2015e99b5/download https://repositorio.cuc.edu.co/bitstreams/92dbcbd9-0045-4cac-8328-52fc6ecb58b8/download https://repositorio.cuc.edu.co/bitstreams/b3e19ea4-643b-4474-873b-25bbf7b3d5da/download https://repositorio.cuc.edu.co/bitstreams/909d4528-3bc9-4418-b137-3e3575eae6be/download |
| bitstream.checksum.fl_str_mv |
42fd4ad1e89814f5e4a476b409eb708c 8a4605be74aa9ea9d79846c1fba20a33 8639e49568d232fce39233cf411e01c8 a6fb85d5bca0610273cd7ce676cbbfd1 d6c3d73146552d8fd2879cfadc570e11 |
| bitstream.checksumAlgorithm.fl_str_mv |
MD5 MD5 MD5 MD5 MD5 |
| repository.name.fl_str_mv |
Repositorio de la Universidad de la Costa CUC |
| repository.mail.fl_str_mv |
repdigital@cuc.edu.co |
| _version_ |
1811760838460047360 |
| spelling |
amelec, viloriaGarcia Padilla, JholmanVargas Mercado, CarlosHernández Palma, HugoORELLANO LLINAS, NATALYARRAZOLA DAVID, MONICA JUDITH2020-01-20T15:09:59Z2020-01-20T15:09:59Z2019-08-190000-2010https://hdl.handle.net/11323/5878Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Dropout, defined as the abandonment of a career before obtaining the corresponding degree, considering a significant time period to rule out the possibility of return. Higher education students´ dropout generates several issues that affect students and universities. The results obtained from the data provided by the Engineering departments of the University of Mumbai, in India, determine that the variables that best explain a student's dropout are the socioeconomic factors and the income score provided by the University Admission Test (UAT). According to the decision tree technique, it is concluded that the retention is 78.3%. The quality of the classifiers allows to ensure that their predictions are correct, with statistical levels of ROC curve are 76%, 75%, and 83% successful for Bayesian network classifiers, decision tree, and neural network respectively.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Garcia Padilla, JholmanVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600Hernández Palma, HugoORELLANO LLINAS, NATALY-will be generated-orcid-0000-0002-5341-6718-600ARRAZOLA DAVID, MONICA JUDITH-will be generated-orcid-0000-0003-0973-8047-600engProcedia Computer Sciencehttps://doi.org/10.1016/j.procs.2019.08.079[1] Vasquez, C., Torres, M., Viloria, A.: Public policies in science and technology in Latin American countries with universities in the top 100 of web ranking. J. Eng. Appl. Sci. 12(11), 2963–2965 (2017).[2] Aguado-López, E., Rogel-Salazar, R., Becerril-García, A., Baca-Zapata, G.: Presencia de universidades en la Red: La brecha digital entre Estados Unidos y el resto del mundo. Revista de Universidad y Sociedad del Conocimiento 6(1), 1–17 (2009).[3] Torres-Samuel, M., Vásquez, C., Viloria, A., Lis-Gutiérrez, J.P., Borrero, T.C., Varela, N.: Web Visibility Profiles of Top100 Latin American Universities. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).[4] Viloria, A., Lis-Gutiérrez, J.P., Gaitán-Angulo, M., Godoy, A.R.M., Moreno, G.C., Kamatkar, S.J.: Methodology for the Design of a Student Pattern Recognition Tool to Facilitate the Teaching – Learning Process Through Knowledge Data Discovery (Big Data). In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943,1-12 (2018).[5] Caicedo, E.J.C., Guerrero, S., López, D.: Propuesta para la construcción de un índice socioeconómico para los estudiantes que presentan las pruebas Saber Pro. Comunicaciones en Estadística, vol. 9(1), 93-106 (2016).[6] Mazón, J.N., Trujillo, J., Serrano, M., Piattini, M.: Designing Data Warehouses: From Business Requirement Analysis to Multidimensional Modeling. In Proceedings of the 1st Int. Workshop on Requirements Engineering for Business Need and IT Alignment. Paris, France (2005).[7] Vásquez, C., Torres-Samuel, M., Viloria, A., Lis-Gutiérrez, J.P., Crissien Borrero, T., Varela, N., Cabrera, D.: Cluster of the Latin American Universities Top100 According to Webometrics 2017. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, Springer, Cham, vol 10943, 1-12 (2018).[8] Haykin, S.: Neural Networks a Comprehensive Foundation. Second Edition. Macmillan College Publishing, Inc. USA. ISBN 9780023527616 (1999).[9] Jain, A. K., Mao, J., Mohiuddin, K. M.: Artificial neural networks: a tutorial. IEEE Computer 29 (3), 1- 32 (1996).[10] Sevim, C., Oztekin, A., Bali, O., Gumus, S., Guresen, E.: Developing an early warning system to predict currency crises. European Journal of Operational Research 237(1), 1095-104 (2014).[11] Sekmen, F., Kurkcu, M.: An Early Warning System for Turkey: The Forecasting of Economic Crisis by Using the Artificial Neural Networks. Asian Economic and Financial Review 4(1), 529-43 (2014).[12] Singhal, D., Swarup, K.S.: Electricity price forecasting using artificial neural networks. IJEPE 33 (1), 550-55 (2011).[13] Mombeini, H., Yazdani-Chamzini, A.: Modelling Gold Price via Artificial Neural Network. Journal of Economics, Business and Management 3 (7), 699-703 (2015).[14] Kulkarni, S., Haidar, I.: Forecasting Model for Crude Oil Price Using Artificial Neural Networks and Commodity Future Prices. International Journal of Computer Science and Information Security 2 (1), 81-89 (2009).[15] Bontempi, G., Ben Taieb, S., Borgne, Y. A.: Machine learning strategies for time series forecasting. In Lecture Notes in Business Information Processing, ed M.-A. Aufaure., and E. Zimányi, Heidelberg: Springer 138 (1), 70-73 (2013).CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2University retentionUniversity dropoutData miningEducationEngineeringBig DataIntegration of data technology for analyzing university dropoutArtículo 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/acceptedVersionPublicationCC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/ac6e5445-57a0-4155-ad27-42601403759a/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/d0db1051-6b36-4e53-852e-3dc2015e99b5/download8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALIntegration of Data Technology for Analyzing University Dropout.pdfIntegration of Data Technology for Analyzing University Dropout.pdfapplication/pdf438102https://repositorio.cuc.edu.co/bitstreams/92dbcbd9-0045-4cac-8328-52fc6ecb58b8/download8639e49568d232fce39233cf411e01c8MD51THUMBNAILIntegration of Data Technology for Analyzing University Dropout.pdf.jpgIntegration of Data Technology for Analyzing University Dropout.pdf.jpgimage/jpeg44091https://repositorio.cuc.edu.co/bitstreams/b3e19ea4-643b-4474-873b-25bbf7b3d5da/downloada6fb85d5bca0610273cd7ce676cbbfd1MD55TEXTIntegration of Data Technology for Analyzing University Dropout.pdf.txtIntegration of Data Technology for Analyzing University Dropout.pdf.txttext/plain23125https://repositorio.cuc.edu.co/bitstreams/909d4528-3bc9-4418-b137-3e3575eae6be/downloadd6c3d73146552d8fd2879cfadc570e11MD5611323/5878oai:repositorio.cuc.edu.co:11323/58782024-09-17 14:08:28.095http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.coTk9URTogUExBQ0UgWU9VUiBPV04gTElDRU5TRSBIRVJFClRoaXMgc2FtcGxlIGxpY2Vuc2UgaXMgcHJvdmlkZWQgZm9yIGluZm9ybWF0aW9uYWwgcHVycG9zZXMgb25seS4KCk5PTi1FWENMVVNJVkUgRElTVFJJQlVUSU9OIExJQ0VOU0UKCkJ5IHNpZ25pbmcgYW5kIHN1Ym1pdHRpbmcgdGhpcyBsaWNlbnNlLCB5b3UgKHRoZSBhdXRob3Iocykgb3IgY29weXJpZ2h0Cm93bmVyKSBncmFudHMgdG8gRFNwYWNlIFVuaXZlcnNpdHkgKERTVSkgdGhlIG5vbi1leGNsdXNpdmUgcmlnaHQgdG8gcmVwcm9kdWNlLAp0cmFuc2xhdGUgKGFzIGRlZmluZWQgYmVsb3cpLCBhbmQvb3IgZGlzdHJpYnV0ZSB5b3VyIHN1Ym1pc3Npb24gKGluY2x1ZGluZwp0aGUgYWJzdHJhY3QpIHdvcmxkd2lkZSBpbiBwcmludCBhbmQgZWxlY3Ryb25pYyBmb3JtYXQgYW5kIGluIGFueSBtZWRpdW0sCmluY2x1ZGluZyBidXQgbm90IGxpbWl0ZWQgdG8gYXVkaW8gb3IgdmlkZW8uCgpZb3UgYWdyZWUgdGhhdCBEU1UgbWF5LCB3aXRob3V0IGNoYW5naW5nIHRoZSBjb250ZW50LCB0cmFuc2xhdGUgdGhlCnN1Ym1pc3Npb24gdG8gYW55IG1lZGl1bSBvciBmb3JtYXQgZm9yIHRoZSBwdXJwb3NlIG9mIHByZXNlcnZhdGlvbi4KCllvdSBhbHNvIGFncmVlIHRoYXQgRFNVIG1heSBrZWVwIG1vcmUgdGhhbiBvbmUgY29weSBvZiB0aGlzIHN1Ym1pc3Npb24gZm9yCnB1cnBvc2VzIG9mIHNlY3VyaXR5LCBiYWNrLXVwIGFuZCBwcmVzZXJ2YXRpb24uCgpZb3UgcmVwcmVzZW50IHRoYXQgdGhlIHN1Ym1pc3Npb24gaXMgeW91ciBvcmlnaW5hbCB3b3JrLCBhbmQgdGhhdCB5b3UgaGF2ZQp0aGUgcmlnaHQgdG8gZ3JhbnQgdGhlIHJpZ2h0cyBjb250YWluZWQgaW4gdGhpcyBsaWNlbnNlLiBZb3UgYWxzbyByZXByZXNlbnQKdGhhdCB5b3VyIHN1Ym1pc3Npb24gZG9lcyBub3QsIHRvIHRoZSBiZXN0IG9mIHlvdXIga25vd2xlZGdlLCBpbmZyaW5nZSB1cG9uCmFueW9uZSdzIGNvcHlyaWdodC4KCklmIHRoZSBzdWJtaXNzaW9uIGNvbnRhaW5zIG1hdGVyaWFsIGZvciB3aGljaCB5b3UgZG8gbm90IGhvbGQgY29weXJpZ2h0LAp5b3UgcmVwcmVzZW50IHRoYXQgeW91IGhhdmUgb2J0YWluZWQgdGhlIHVucmVzdHJpY3RlZCBwZXJtaXNzaW9uIG9mIHRoZQpjb3B5cmlnaHQgb3duZXIgdG8gZ3JhbnQgRFNVIHRoZSByaWdodHMgcmVxdWlyZWQgYnkgdGhpcyBsaWNlbnNlLCBhbmQgdGhhdApzdWNoIHRoaXJkLXBhcnR5IG93bmVkIG1hdGVyaWFsIGlzIGNsZWFybHkgaWRlbnRpZmllZCBhbmQgYWNrbm93bGVkZ2VkCndpdGhpbiB0aGUgdGV4dCBvciBjb250ZW50IG9mIHRoZSBzdWJtaXNzaW9uLgoKSUYgVEhFIFNVQk1JU1NJT04gSVMgQkFTRUQgVVBPTiBXT1JLIFRIQVQgSEFTIEJFRU4gU1BPTlNPUkVEIE9SIFNVUFBPUlRFRApCWSBBTiBBR0VOQ1kgT1IgT1JHQU5JWkFUSU9OIE9USEVSIFRIQU4gRFNVLCBZT1UgUkVQUkVTRU5UIFRIQVQgWU9VIEhBVkUKRlVMRklMTEVEIEFOWSBSSUdIVCBPRiBSRVZJRVcgT1IgT1RIRVIgT0JMSUdBVElPTlMgUkVRVUlSRUQgQlkgU1VDSApDT05UUkFDVCBPUiBBR1JFRU1FTlQuCgpEU1Ugd2lsbCBjbGVhcmx5IGlkZW50aWZ5IHlvdXIgbmFtZShzKSBhcyB0aGUgYXV0aG9yKHMpIG9yIG93bmVyKHMpIG9mIHRoZQpzdWJtaXNzaW9uLCBhbmQgd2lsbCBub3QgbWFrZSBhbnkgYWx0ZXJhdGlvbiwgb3RoZXIgdGhhbiBhcyBhbGxvd2VkIGJ5IHRoaXMKbGljZW5zZSwgdG8geW91ciBzdWJtaXNzaW9uLgo= |