Evaluación de objetos digitales de aprendizaje musical en Moodle
Autor:
Espigares Pinazo, Manuel Jesús (1)
; Bautista Vallejo, José Manuel
Fecha:
11/2018Palabra clave:
Tipo de Ítem:
Articulo Revista IndexadaDirección web:
https://revistas.um.es/educatio/article/view/350051
Resumen:
Este estudio presenta la aplicación de procesos automatizados de análisis a objetos digitales de aprendizaje musical online, mediados por una plataforma telemática. A nivel teórico, se basa en la aplicación de los principios del aprendizaje virtual, la educación personalizada y la aplicación de procesos automatizados de análisis de datos educativos. En concreto, se plantea la aplicación de dichas técnicas a los test de evaluación inicial, para medir niveles de conocimientos previos en la materia. El análisis de la información se efectúa a partir de los datos recogidos en una herramienta para la elaboración de cursos online, Moodle. A partir de dichos datos se halla un modelo, denominado k-medias (k-means en lengua inglesa), que permite clasificar los diferentes niveles de conocimientos musicales. El modelo elaborado establece tres perfiles en cuanto al nivel de aprendizaje musical del alumnado: nivel alto, medio y bajo.
Descripción:
This study presents the application of automated analysis processes to digital objects in online music learning, mediated through a telematic platform. Theoretically, the study is based on the application of the principles of e-learning, personalized education and implementation of automated processes for analysing educational data. Specifically, this study taps into the application of such techniques to initial test assessment, with a view to measuring levels of subject knowledge. The analysis of the information is undertaken from the data collected in a tool for making online courses, Moodle. From the analysis of these data a model, called k-means, emerges which classifies the different levels of musical knowledge. The model establishes three profiles regarding acquisition of music knowledge: high, medium and low.
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