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

Kamsa, Imane; Elouahbi, Rachid; Khoukhi, Fatima El; Karite, Touria; Zouiten, Hayat

doi:10.1109/ithet.2016.7760697

Cited by 17 publications

(16 citation statements)

References 6 publications

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“…Analogously to the principle of ant colony algorithm, a probabilistic selection procedure was introduced in our approach to preserving the basis of random exploration of all arcs while pulling the most relevant regarding the value of the evaluation function F [32,33].…”

Section: F Recommendation Of the Path According To A Probability Selmentioning

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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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: F Recommendation Of the Path According To A Probability Selmentioning

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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“…To model the possible path that the learner can follow in order to complete his pedagogical objective, we choose a pedagogical graph as a model defined by the triplet G = (V, E, V °) [18] (Fig. 2).…”

Section: A Modeling the Learning Pedagogical Graphmentioning

confidence: 99%

Using The Intelligent Agents for Planning The Learning Time in A Distance Learning System

Kamsa

Elouahbi

Khoukhi

2017

International Journal on Advanced Science, Engineering and Information Technology

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In this paper, Intelligent Agent (IA) and Pedagogical Graph (PG) are combined to plan a personalized timetable and regulate the disturbances produced on this calendar of learning. This work is done by three intelligent agents: Scheduler Agent (SA), Regulatory Agent (RA), and Decision Agent (DA). The function of SA is to calculate the period of each learning unit modeling in the pedagogical graph for each learner before starting his learning process. This operation takes into account the understanding speed, the rhythm, the periodic learning time, the expected time of learning units and the learning path regulations. The work of RA and DA is to adjust and take the decision to update timetable bearing in mind the impact of its perturbation and the chance given to the learner. The major benefit of this work is always to keep the time flexible in the e-learning while exercising a weak and indirect influence on the learner for optimal learning because the time sequence in learning has a favor impact in the acquisition.

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“…Many research works have been interested in improving educational content presented in e-Learning platforms [1,3,10,15]. Other researches have proposed approaches for learning paths optimization [6,11]. In another stage, some researchers have interested in learners behavior modeling in order to enhance the e-Learning platform performances [23,25,26].…”

Section: Introductionmentioning

confidence: 99%

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

Essayah¹,

Youssfi²,

Bouattane³

et al. 2019

IJACSA

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In this paper, we present a new model of e-Learning platforms based on semantic micro services using discovery, selection and composition methods to generate learning paths. In this model, each semantic micro service represents an elementary educational resource that can be a course, an exercise, a tutorial or an evaluation implementing a precise learning path objective. The semantic micro services are described using ontologies and deployed in multi-instances in a cloud environment according to a load balancing and a fault tolerance system. Learners' requests are sent to a proxy micro service having learning paths abstract structures represented as an oriented graph. Proxy micro service analyses the request to define the learner profile and context in order to provide him with the semantic micro services responsible of the educational resources satisfying his functional and non-functional needs. In this model, to achieve an optimal learning path generation a two steps process is employed, where local optimization uses semantic discovery and selection based on a matchmaking algorithm and a quality of service measurement, and global optimization adopts an ant colony optimization algorithm to select the best resource combination. Our experimental results show that the proposed model can effectively returns optimized learning paths considering individual, collective and pedagogical factors.

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

Cited by 17 publications

References 6 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

Using The Intelligent Agents for Planning The Learning Time in A Distance Learning System

QoS-based Semantic Micro Services Discovery and Composition using ACO Algorithm

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