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 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.

• 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