Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data

Romero, Cristobal and González, P. and Ventura, Sebastian and del Jesus, M.J. and Herrera, F. Evolutionary algorithms for subgroup discovery in e-learning: A practical application using Moodle data. Expert Systems with Applications, 36 (2). pp. 1632-1644. ISSN 0957-4174

[img]
Preview
Text
Romero (2009) Evolutionary algorithms for subgroup discovery in e-learning- A practical application using Moodle data.pdf

Download (193kB) | Preview
Official URL: http://www.sciencedirect.com/science/article/pii/S...

Abstract

This work describes the application of subgroup discovery using evolutionary algorithms to the usage data of the Moodle course management system, a case study of the University of Cordoba, Spain. The objective is to obtain rules which describe relationships between the student’s usage of the different activities and modules provided by this e-learning system and the final marks obtained in the courses. We use an evolutionary algorithm for the induction of fuzzy rules in canonical form and disjunctive normal form. The results obtained by different algorithms for subgroup discovery are compared, showing the suitability of the evolutionary subgroup discovery to this problem.

Item Type: Article
Uncontrolled Keywords: Web-based education, Subgroup discovery, Evolutionary algorithms, Fuzzy rules
Subjects: Educational technology > Learning analytics
Divisions: Higher education, Universities, Vocational training, Colleges
Depositing User: Dr Michael de Raadt
Date Deposited: 01 Feb 2016 00:45
Last Modified: 01 Feb 2016 00:45
URI: http://research.moodle.net/id/eprint/110

Actions (login required)

View Item View Item