Determinating student interactions in a virtual learning environment using data mining

This article focuses on determining the students´ interactions in the Virtual English Course with Distance Education Model (DEM) at Mumbai University, in India. For this purpose, an analysis was carried out on the database of the students during the academic period 2015 - 2018 to select the necessar...

Full description

Autores:
Amelec, Viloria
Rodríguez López, Jorge
Payares, Karen
Vargas Mercado, Carlos
Ethel Duran, Sonia
Hernández-Palma, Hugo
Arrozola David, Mónica
Duran, Sonia Ethel
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:11323/5838
Acceso en línea:
https://hdl.handle.net/11323/5838
https://repositorio.cuc.edu.co/
Palabra clave:
Data mining
Classification technique
Model algorithm
Methodology
Minería de datos
Técnica de clasificación
Modelo algoritmo
Metodología
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_72edc2454f952345ae10a578c50b3c99
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5838
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Determinating student interactions in a virtual learning environment using data mining
dc.title.translated.spa.fl_str_mv Determinación de las interacciones de los estudiantes en un entorno de aprendizaje virtual mediante minería de datos
title Determinating student interactions in a virtual learning environment using data mining
spellingShingle Determinating student interactions in a virtual learning environment using data mining
Data mining
Classification technique
Model algorithm
Methodology
Minería de datos
Técnica de clasificación
Modelo algoritmo
Metodología
title_short Determinating student interactions in a virtual learning environment using data mining
title_full Determinating student interactions in a virtual learning environment using data mining
title_fullStr Determinating student interactions in a virtual learning environment using data mining
title_full_unstemmed Determinating student interactions in a virtual learning environment using data mining
title_sort Determinating student interactions in a virtual learning environment using data mining
dc.creator.fl_str_mv Amelec, Viloria
Rodríguez López, Jorge
Payares, Karen
Vargas Mercado, Carlos
Ethel Duran, Sonia
Hernández-Palma, Hugo
Arrozola David, Mónica
Duran, Sonia Ethel
dc.contributor.author.spa.fl_str_mv Amelec, Viloria
Rodríguez López, Jorge
Payares, Karen
Vargas Mercado, Carlos
Ethel Duran, Sonia
Hernández-Palma, Hugo
Arrozola David, Mónica
dc.contributor.author.none.fl_str_mv Duran, Sonia Ethel
dc.subject.spa.fl_str_mv Data mining
Classification technique
Model algorithm
Methodology
Minería de datos
Técnica de clasificación
Modelo algoritmo
Metodología
topic Data mining
Classification technique
Model algorithm
Methodology
Minería de datos
Técnica de clasificación
Modelo algoritmo
Metodología
description This article focuses on determining the students´ interactions in the Virtual English Course with Distance Education Model (DEM) at Mumbai University, in India. For this purpose, an analysis was carried out on the database of the students during the academic period 2015 - 2018 to select the necessary attributes that allowed to generate a data mining model. An analysis of the mining methods was subsequently carried out comparing each of them in order to select the one that helps the development of the project, choosing the Crisp-dm method since it contains multiple phases indicating each activity to be completed, thus becoming a practical guide. In addition, a comparative analysis was developed taking into account features of the data mining tools where RapidMiner was selected to perform the processes using some algorithms along with the student data which were divided into two sets for training and validation, resulting the decision tree as the best algorithm for the purpose as it correctly classified the instances with a minimum margin of error.
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-01-16T14:14:16Z
dc.date.available.none.fl_str_mv 2020-01-16T14:14:16Z
dc.type.spa.fl_str_mv Artículo de revista
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identifier_str_mv 1877-0509
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5838
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1016/j.procs.2019.08.082
dc.relation.references.spa.fl_str_mv Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) 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, vol 10943. Springer, Cham.
Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012).
Ben Salem, S., Naouali, S., Chtourou, Z., 2018. A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463–483. https://doi.org/10.1016/j.compeleceng.2018.04.023.
Chakraborty, S., Das, S., 2018. Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148– 156. https://doi.org/10.1016/j.spl.2018.01.015
Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M., 2018. Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (Ny). https://doi.org/10.1016/j.ins.2018.07.034
Rahman, M.A., Islam, M.Z., Bossomaier, T., 2015. ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means. J. King Saud Univ. - Comput. Inf. Sci. 27, 113–128. https://doi.org/10.1016/j.jksuci.2014.04.002
Rahman, M.A., Islam, M.Z., 2014. A hybrid clustering technique combining a novel genetic algorithm with K-Means. Knowledge-Based Syst. 71, 345–365.
Ramadas, M., Abraham, A., Kumar, S., 2016. FSDE-Forced Strategy Differential Evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2016.12.005
Yaqian, Z., Chai, Q.H., Boon, G.W., 2017. Curvature-based method for determining the number of clusters. Inf. Sci. (Ny). https://doi.org/10.1016/j.ins.2017.05.024
Tîrnăucă, C., Gómez-Pérez, D., Balcázar, J.L., Montaña, J.L., 2018. Global optimality in k-means clustering. Inf. Sci. (Ny). 439–440, 79–94. https://doi.org/10.1016/j.ins.2018.02.001
Xiang, W., Zhu, N., Ma, S., Meng, X., An, M., 2015. A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing. https://doi.org/10.1016/j.neucom.2015.01.058
Torres-Samuel M., Vásquez C.L., Viloria A., Varela N., Hernández-Fernandez L., Portillo-Medina R. (2018) Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
Vásquez, 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)
Torres-Samuel M, Carmen Vásquez, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Danelys Cabrera, Mercedes GaitánAngulo, Jenny-Paola Lis-Gutiérrez. (2018). Efficiency Analysis of the Visibility of Latin American Universities and Their Impact on the Ranking Web. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
Piotrowski, A.P., 2017. Review of Differential Evolution population size. Swarm Evol. Comput. 32, 1–24. https://doi.org/10.1016/j.swevo.2016.05.003
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institution Corporación Universidad de la Costa
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spelling Amelec, ViloriaRodríguez López, JorgePayares, KarenVargas Mercado, CarlosEthel Duran, SoniaHernández-Palma, HugoArrozola David, MónicaDuran, Sonia Ethelvirtual::543-12020-01-16T14:14:16Z2020-01-16T14:14:16Z20191877-0509https://hdl.handle.net/11323/5838Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/This article focuses on determining the students´ interactions in the Virtual English Course with Distance Education Model (DEM) at Mumbai University, in India. For this purpose, an analysis was carried out on the database of the students during the academic period 2015 - 2018 to select the necessary attributes that allowed to generate a data mining model. An analysis of the mining methods was subsequently carried out comparing each of them in order to select the one that helps the development of the project, choosing the Crisp-dm method since it contains multiple phases indicating each activity to be completed, thus becoming a practical guide. In addition, a comparative analysis was developed taking into account features of the data mining tools where RapidMiner was selected to perform the processes using some algorithms along with the student data which were divided into two sets for training and validation, resulting the decision tree as the best algorithm for the purpose as it correctly classified the instances with a minimum margin of error.Este artículo se centra en determinar las interacciones de los estudiantes en el Curso de inglés virtual con el Modelo de educación a distancia (DEM) en la Universidad de Mumbai, en India. Para este propósito, se realizó un análisis en la base de datos de los estudiantes durante el período académico 2015-2018 para seleccionar los atributos necesarios que permitieron generar un modelo de minería de datos. Posteriormente se realizó un análisis de los métodos de minería comparando cada uno de ellos con el fin de seleccionar el que ayude al desarrollo del proyecto, eligiendo el método Crisp-dm ya que contiene múltiples fases que indican cada actividad a completar, convirtiéndose así en una práctica guía. Además, se desarrolló un análisis comparativo teniendo en cuenta las características de las herramientas de minería de datos en las que se seleccionó RapidMiner para realizar los procesos utilizando algunos algoritmos junto con los datos del alumno que se dividieron en dos conjuntos para capacitación y validación, dando como resultado el árbol de decisión como el mejor algoritmo para este propósito, ya que clasificó correctamente las instancias con un margen de error mínimo.Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Rodríguez López, JorgePayares, KarenVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600Ethel Duran, SoniaHernández-Palma, HugoArrozola David, MónicaengProcedia Computer Sciencehttps://doi.org/10.1016/j.procs.2019.08.082Viloria A., Lis-Gutiérrez JP., Gaitán-Angulo M., Godoy A.R.M., Moreno G.C., Kamatkar S.J. (2018) 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, vol 10943. Springer, Cham.Ballesteros Román, A.: Minería de Datos Educativa Aplicada a la Investigación de Patrones de Aprendizaje en Estudiante en Ciencias. Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, México City (2012).Ben Salem, S., Naouali, S., Chtourou, Z., 2018. A fast and effective partitional clustering algorithm for large categorical datasets using a k-means based approach. Comput. Electr. Eng. 68, 463–483. https://doi.org/10.1016/j.compeleceng.2018.04.023.Chakraborty, S., Das, S., 2018. Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian means algorithm. Stat. Probab. Lett. 137, 148– 156. https://doi.org/10.1016/j.spl.2018.01.015Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M., 2018. Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (Ny). https://doi.org/10.1016/j.ins.2018.07.034Rahman, M.A., Islam, M.Z., Bossomaier, T., 2015. ModEx and Seed-Detective: Two novel techniques for high quality clustering by using good initial seeds in K-Means. J. King Saud Univ. - Comput. Inf. Sci. 27, 113–128. https://doi.org/10.1016/j.jksuci.2014.04.002Rahman, M.A., Islam, M.Z., 2014. A hybrid clustering technique combining a novel genetic algorithm with K-Means. Knowledge-Based Syst. 71, 345–365.Ramadas, M., Abraham, A., Kumar, S., 2016. FSDE-Forced Strategy Differential Evolution used for data clustering. J. King Saud Univ. - Comput. Inf. Sci. https://doi.org/10.1016/j.jksuci.2016.12.005Yaqian, Z., Chai, Q.H., Boon, G.W., 2017. Curvature-based method for determining the number of clusters. Inf. Sci. (Ny). https://doi.org/10.1016/j.ins.2017.05.024Tîrnăucă, C., Gómez-Pérez, D., Balcázar, J.L., Montaña, J.L., 2018. Global optimality in k-means clustering. Inf. Sci. (Ny). 439–440, 79–94. https://doi.org/10.1016/j.ins.2018.02.001Xiang, W., Zhu, N., Ma, S., Meng, X., An, M., 2015. A dynamic shuffled differential evolution algorithm for data clustering. Neurocomputing. https://doi.org/10.1016/j.neucom.2015.01.058Torres-Samuel M., Vásquez C.L., Viloria A., Varela N., Hernández-Fernandez L., Portillo-Medina R. (2018) Analysis of Patterns in the University World Rankings Webometrics, Shanghai, QS and SIR-SCimago: Case Latin America. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.Vásquez, 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)Torres-Samuel M, Carmen Vásquez, Amelec Viloria, Tito Crissien Borrero, Noel Varela, Danelys Cabrera, Mercedes GaitánAngulo, Jenny-Paola Lis-Gutiérrez. (2018). Efficiency Analysis of the Visibility of Latin American Universities and Their Impact on the Ranking Web. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.Piotrowski, A.P., 2017. Review of Differential Evolution population size. Swarm Evol. Comput. 32, 1–24. https://doi.org/10.1016/j.swevo.2016.05.003CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Data miningClassification techniqueModel algorithmMethodologyMinería de datosTécnica de clasificaciónModelo algoritmoMetodologíaDeterminating student interactions in a virtual learning environment using data miningDeterminación de las interacciones de los estudiantes en un entorno de aprendizaje virtual mediante minería de datosArtículo 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/acceptedVersionPublication8a4be9eb-d4d6-4227-80f0-519c476008c0virtual::543-18a4be9eb-d4d6-4227-80f0-519c476008c0virtual::543-1https://scholar.google.com.co/citations?user=u0ptRmUAAAAJ&hl=esvirtual::543-10000-0002-0876-7850virtual::543-1LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/7659a2a1-373d-4b78-bd2d-137c03ce148f/download8a4605be74aa9ea9d79846c1fba20a33MD53ORIGINALDeterminating Student Interactions.pdfDeterminating Student Interactions.pdfapplication/pdf448370https://repositorio.cuc.edu.co/bitstreams/e2e58b9c-bc7f-4105-a05e-d531cd4184c1/downloadf0eae25fadfd1a8bc4359539c1d248bfMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/bef730f0-9b68-4eaa-9a92-7b7f887bbf36/download42fd4ad1e89814f5e4a476b409eb708cMD52THUMBNAILDeterminating Student Interactions.pdf.jpgDeterminating Student Interactions.pdf.jpgimage/jpeg48440https://repositorio.cuc.edu.co/bitstreams/3b906c84-1925-4bc1-8c75-b11af3aefc39/download615d119987bb58807f4eaaa6a3cbb21bMD55TEXTDeterminating Student Interactions.pdf.txtDeterminating Student Interactions.pdf.txttext/plain23169https://repositorio.cuc.edu.co/bitstreams/9bf352b2-0a5c-4039-bde6-79ede55d7f10/downloadff1df468ed3e7ef8cf9f401f2fd796ecMD5611323/5838oai:repositorio.cuc.edu.co:11323/58382025-02-20 14:01:59.354http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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