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http://hdl.handle.net/10609/99647
Title: Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación
Other Titles: Methodology of Machine Learning for the classification and Prediction of users in Virtual Education Environments
Author: De la Hoz Domínguez, Enrique José
De la Hoz Granadillo, Efraín
Fontalvo Herrera, Tomás
Keywords: Cluster
VLE
Machine learning
KNN
Education
Issue Date: 2-Feb-2019
Publisher: Información Tecnológica
Citation: De la Hoz, E.J., De la Hoz, E. & Fontalvo, T. (2019). Metodología de Aprendizaje Automático para la Clasificación y Predicción de Usuarios en Ambientes Virtuales de Educación. Información Tecnológica, 30(1), 247-254. doi: 10.4067/S0718-07642019000100247
Also see: https://scielo.conicyt.cl/pdf/infotec/v30n1/0718-0764-infotec-30-01-247.pdf
Abstract: A methodology to classify and predict users in virtual education environments, studying the interaction of students with the platform and their performance in exams is proposed. For this, the machine learning tools, main components, clustering, fuzzy and the algorithm of the K nearest neighbor were used. The methodology first relates the users according to the study variables, to then implement a cluster analysis that identifies the formation of groups. Finally uses a machine learning algorithm to classify the users according to their level of knowledge. The results show how the time a student stays in the platform is not related to belonging to the high knowledge group. Three categories of users were identified, applying the Fuzzy K-means methodology to determine transition zones between levels of knowledge. The k nearest neighbor algorithm presents the best prediction results with 91%.
Language: Spanish
URI: http://hdl.handle.net/10609/99647
ISSN: 0718-0764MIAR
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