Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems

Date

2012-09-01

Advisors

Journal Title

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ISSN

DOI

Volume Title

Publisher

DMU

Type

Technical Report

Peer reviewed

No

Abstract

Nowadays, there is a increasing in the use of learning management systems from the universities. This type of systems are also known under other di erent terms as course management systems or learning content management systems. Speci cally, these systems are e-learning platforms o ering di erent facilities for information sharing and communication between the participants in the e-learning process. This contribution presents an experimental study with several subgroup discovery algorithms based on evolutionary fuzzy systems using data from a web-based education system. The main objective of this contribution is to extract unusual subgroups to describe possible relationships between the use of the e-learning platform and marks obtained by the students. The results obtained by the best performing algorithm, NMEEF-SD, are also presented. The most representative results obtained by this algorithm are summarised in order to obtain knowledge that can allow teachers to take actions to improve student performance.

Description

Keywords

Learning Systems, e-learning, evolutionary fuzzy systems

Citation

Carmona, C. J. and Elizondo, D. (2012) Analysing the Moodle e-learning platform through subgroup discovery algorithms based on evolutionary fuzzy systems.

Rights

Research Institute

Institute of Artificial Intelligence (IAI)