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Information 2015, 6(3), 564-575; doi:10.3390/info6030564

Toward E-Content Adaptation: Units’ Sequence and Adapted Ant Colony Algorithm

1
LIMIARF and STRS Laboratories, Faculty of Science, Mohammed V University, Avenue Al Oumam AlMouttahida, 10500 Rabat, Morocco
2
STRS Laboratory, National Institute of Posts and Telecommunication (INPT), 2, Avenue Allal EL Fassi-Madinat AL Irfane, 10000 Rabat, Morocco
*
Author to whom correspondence should be addressed.
Academic Editor: Ahmed El Oualkadi
Received: 24 June 2015 / Revised: 24 June 2015 / Accepted: 7 August 2015 / Published: 1 September 2015
(This article belongs to the Special Issue Selected Papers from MedICT 2015)
View Full-Text   |   Download PDF [937 KB, uploaded 1 September 2015]   |  

Abstract

An adapted ant colony algorithm is proposed to adapt e-content to learner’s profile. The pertinence of proposed units keeps learners motivated. A model of categorization of course’s units is presented. Two learning paths are discussed based on a predefined graph. In addition, the ant algorithm is simulated on the proposed model. The adapted algorithm requires a definition of a new pheromone which is a parameter responsible for defining whether the unit is in the right pedagogical sequence or in the wrong one. Moreover, it influences the calculation of quantity of pheromone deposited on each arc. Accordingly, results show that there are positive differences in learner’s passages to propose the suitable units depending on the sequence and the number of successes. The proposed units do not depend on the change of number of units around 10 to 30 units in the algorithm process. View Full-Text
Keywords: content adaptation; ant colony algorithm; course design; evaluation; learner’s profile content adaptation; ant colony algorithm; course design; evaluation; learner’s profile
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Benabdellah, N.C.; Gharbi, M.; Bellafkih, M. Toward E-Content Adaptation: Units’ Sequence and Adapted Ant Colony Algorithm. Information 2015, 6, 564-575.

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