Ant colony optimization for competency based learning objects sequencing in e-learning

doi:10.1016/j.amc.2015.04.067

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Ant colony optimization for competency based learning objects sequencing in e-learning

2015-07-01

PRE

A. Priya Dharshini *

S. Chandrakumarmangalam

G. Arthi

DOI：10.1016/j.amc.2015.04.067

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Abstract

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E-learning is a knowledge management concept where content creators have to arrange a set of learning resources, to present them in a clear and comprehensive way to the learners. In this paper, we formulate a new approach for obtaining better learning paths for different learners groups as a constraint satisfaction problem (CSP) in which meta data and competencies are used to define the relationships between the learning objects (LOs), where the course materials are used to formulate LOs sequence. The main aim of this paper is to obtain a dynamic learning path for the considered CSP problem by using the swarm intelligence technique, which is a sub set of the artificial intelligence technique. Further, the proposed model is tested in a simulated environment, which gives an optimized LO sequencing. The simulation results reveal that the artificial ants gives solution to the proposed problem in an optimized way. More precisely, suitable learning path can be obtained by applying ant colony optimization (ACO) technique. From the obtained results it is concluded that the proposed model supports the e-learning portal administrator in getting benefits in terms of less processing tulle and minimal sequencing cost. (C) 2015 Elsevier Inc. All rights reserved.

Keywords:

Swarm intelligence

Optimization

Knowledge management

E-Learning

Learning object sequencing

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