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dc.creatorAmelec, Viloria
dc.creatorRodríguez López, Jorge
dc.creatorPayares, Karen
dc.creatorVargas Mercado, Carlos
dc.creatorEthel Duran, Sonia
dc.creatorHernández-Palma, Hugo
dc.creatorArrozola David, Mónica
dc.date.accessioned2020-01-16T14:14:16Z
dc.date.available2020-01-16T14:14:16Z
dc.date.issued2019
dc.identifier.issn1877-0509
dc.identifier.urihttp://hdl.handle.net/11323/5838
dc.description.abstractThis 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.spa
dc.description.abstractEste 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.spa
dc.language.isoengspa
dc.publisherProcedia Computer Sciencespa
dc.relation.ispartofhttps://doi.org/10.1016/j.procs.2019.08.082spa
dc.rightsCC0 1.0 Universal*
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/*
dc.subjectData miningspa
dc.subjectClassification techniquespa
dc.subjectModel algorithmspa
dc.subjectMethodologyspa
dc.subjectMinería de datosspa
dc.subjectTécnica de clasificaciónspa
dc.subjectModelo algoritmospa
dc.subjectMetodologíaspa
dc.titleDeterminating student interactions in a virtual learning environment using data miningspa
dc.title.alternativeDeterminación de las interacciones de los estudiantes en un entorno de aprendizaje virtual mediante minería de datosspa
dc.typeArticlespa
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dc.type.hasVersioninfo:eu-repo/semantics/submittedVersionspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa


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