An approach to automatic learning assessment based on the computational theory of perceptions

Sanchez Torrubia, Maria Gloria; Torres Blanc, Carmen y Triviño Barros, Gracián (2012). An approach to automatic learning assessment based on the computational theory of perceptions. "Expert Systems with Applications", v. 39 (n. 15); pp. 12177-12191. ISSN 0957-4174. https://doi.org/10.1016/j.eswa.2012.04.069.

Descripción

Título: An approach to automatic learning assessment based on the computational theory of perceptions
Autor/es:
  • Sanchez Torrubia, Maria Gloria
  • Torres Blanc, Carmen
  • Triviño Barros, Gracián
Tipo de Documento: Artículo
Título de Revista/Publicación: Expert Systems with Applications
Fecha: 1 Noviembre 2012
Volumen: 39
Materias:
Palabras Clave Informales: Automatic learning assessment, Computing with words and perceptions, Granular linguistic model of a phenomenon, Evaluación del aprendizaje automática, Informática con palabras y percepciones, Modelo lingüístico y granular de un fenómeno.
Escuela: Facultad de Informática (UPM) [antigua denominación]
Departamento: Matemática Aplicada
Licencias Creative Commons: Reconocimiento - Sin obra derivada - No comercial

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Resumen

E-learning systems output a huge quantity of data on a learning process. However, it takes a lot of specialist human resources to manually process these data and generate an assessment report. Additionally, for formative assessment, the report should state the attainment level of the learning goals defined by the instructor. This paper describes the use of the granular linguistic model of a phenomenon (GLMP) to model the assessment of the learning process and implement the automated generation of an assessment report. GLMP is based on fuzzy logic and the computational theory of perceptions. This technique is useful for implementing complex assessment criteria using inference systems based on linguistic rules. Apart from the grade, the model also generates a detailed natural language progress report on the achieved proficiency level, based exclusively on the objective data gathered from correct and incorrect responses. This is illustrated by applying the model to the assessment of Dijkstra’s algorithm learning using a visual simulation-based graph algorithm learning environment, called GRAPHs

Más información

ID de Registro: 15804
Identificador DC: http://oa.upm.es/15804/
Identificador OAI: oai:oa.upm.es:15804
Identificador DOI: 10.1016/j.eswa.2012.04.069
URL Oficial: http://www.elsevier.com/locate/eswa
Depositado por: Memoria Investigacion
Depositado el: 17 Jun 2013 16:57
Ultima Modificación: 21 Abr 2016 16:06
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