Application d’une approche inspirée des colonies de fourmis pour la recommandation des chemins d’apprentissage dans un cours en ligne : modèle et expérience

Dahbi, Aziz; Kamoun, Najib El; Aqqal, Abdelhak; Hannani, Asmaa El

doi:10.7202/1035632ar

Cited by 3 publications

(2 citation statements)

References 14 publications

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“…• In the work of Talhi et al [10] is presented a generic guardian canvas, which integrates a technique of intelligent tutors and those of the hypermedia systems in the design of guidance, assistance, and adaptation of courses to different learners. • The work of Dahbi et al [11] proposes an approach of the recommendation of learning path in an online course through the adjustment of ant colony optimization. This approach is based both on the recommendation of relative paths by the teacher and the results stored progressively by learners on borrowed paths.…”

Section: B Guidance and Optimization In E-learningmentioning

confidence: 99%

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The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

Kamsa

Elouahbi

Khoukhi

2018

International Journal on Advanced Science, Engineering and Information Technology

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The approach that we propose in this paper is part of the optimization of the learning path in the e-learning environment. It relates more precisely to the adaptation and the guidance of the learners according to, on the one hand, their needs and cognitive abilities and, on the other hand, the collective experience of co-learners. This work is done by an optimizer agent that has the specificity to provide to each learner the best path from the beginning of the learning process to its completion. The optimization of this approach is determined automatically and dynamically, by seeking the path that is more marked by success. This determination is concluding according to the vision of the pedagogical team and the collective experience of the learners. At the same time, we search of the path that is more adapted to the specificities of the learner regarding preferences, level of knowledge and learner history. This operation is accomplished by exploiting their profile for perfect customization, and the adaptation of ant colony algorithm for guidance tends towards maximizing the acquisition of the learner. The design of our work is unitary; it is based on the integration of individual, collective factors of the learner. Moreover, the results are very conclusive. They show that the proposed approach can efficiently select the optimal path and that it participates fully in the satisfaction and success of the learner.

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Section: B Guidance and Optimization In E-learningmentioning

confidence: 99%

“…and = . (11) where NB_ is the number of the visitor of and is the number of visitor of such as ϵto the path learner.…”

Section: = (10)mentioning

confidence: 99%

The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

Kamsa

Elouahbi

Khoukhi

2018

International Journal on Advanced Science, Engineering and Information Technology

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Optimizing collaborative learning path by ant's optimization technique in e-learning system

Kamsa

Elouahbi

Khoukhi

et al. 2016

2016 15th International Conference on Information Technology Based Higher Education and Training (ITHET)

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The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

Hamza

Tlili

2018

International Journal of Web-Based Learning and Teaching Technologies

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This article addresses the learning style as a criterion for optimization of adaptive content in hypermedia applications. First, the authors present the different optimization approaches proposed in the area of adaptive hypermedia systems whose goal is to define the optimization problem in this type of system. Then, they present the architecture of their proposed system. The first step involves choosing a learning style model. The selection of this style is done by using a dedicated questionnaire answered by a learner. Then a modeling of the learner is completed based on his learning style. Finally, content that is to be presented to the learner is managed by a content generator module, depending on the model of the learner. Built on methods and techniques proposed for modeling and adaptation, the adaptive hypermedia system based on learning styles provides optimized adaptations. The authors' approach has been experimentally validated and the results are encouraging.

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Application d’une approche inspirée des colonies de fourmis pour la recommandation des chemins d’apprentissage dans un cours en ligne : modèle et expérience

Cited by 3 publications

References 14 publications

The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

The Combination between the Individual Factors and the Collective Experience for Ultimate Optimization Learning Path using Ant Colony Algorithm

Optimizing collaborative learning path by ant's optimization technique in e-learning system

The Optimization by Using the Learning Styles in the Adaptive Hypermedia Applications

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