Differential evolution clustering and data mining for determining learning routes in moodle

Data mining techniques are being widely used in the field of education from the arise of e-learning platforms like Moodle, WebCT, Claroline, and others, and the virtual learning system they entail. Information systems store all activities in files or databases which, correctly processed, may offer relevant data to the teacher. This paper reports the use of data mining techniques and Differential Evolution Clustering for discovering learning routes frequently applied in the Moodle Platform. Data were obtained form 4.115 university students monitored in an online course using Moodle 3.1. Firstly, students were grouped according to the data from a final qualifications report in a course. Secondly, the data of the Moodle logs about each cluster/group of students was used separately with the aim of obtaining more specific and precise models of the students behavior in the processes. © 2019, Springer Nature Singapore Pte Ltd.

• Autores: Viloria, Amelec; Crissien Borrero, Tito; Vargas Villa, Jesús; Torres, Maritza; García Guiliany, Jesús; Vargas Mercado, Carlos; Orellano Llinas, Nataly; Batista Zea, Karina
• Tipo de recurso: Article of investigation
• Fecha de publicación: 2019
• Institución: Corporación Universidad de la Costa
• Repositorio: REDICUC - Repositorio CUC
• Idioma: eng
• OAI Identifier: oai:repositorio.cuc.edu.co:11323/10748
• Acceso en línea: https://hdl.handle.net/11323/10748; https://repositorio.cuc.edu.co/
• Palabra clave: Clustering; Data mining; Differential evolution; K-means; Moodle
• Rights: closedAccess
• License: Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)