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A Predictive System for Distance Learning Based on Ontologies and Data Mining

Authors:
Evangelia Boufardea
John Garofalakis

Keywords: ontology; protégé; RDF; data mining; weka; classification; J48 algorithm; distance learning; HOU

Abstract:
The development of distance learning, e-learning and online learning, has increased exponentially in recent years. Lately, researchers have begun to investigate various data mining techniques in order to improve the quality of this type of education. However, although distance learning in education is well established, there are a few attempts to extract educationally useful information during the course and before the final evaluation. In this paper, we propose an ontology based on the structure of a distance learning environment which enriches a recommendation system with rules generated by data mining techniques. Tutors can use this recommendation system in order to predict learners’ progress and their final performance. This application will enhance the efficiency of any distance learning or e-learning platform and will be beneficial for learners as well as for tutors in the learning process.

Pages: 151 to 158

Copyright: Copyright (c) IARIA, 2012

Publication date: July 22, 2012

Published in: conference

ISSN: 2308-4197

ISBN: 978-1-61208-218-9

Location: Nice, France

Dates: from July 22, 2012 to July 27, 2012