An Automatic Updating Process to Control The E-learning Courseware

Elghibari, Fatiha; Elouahbi, Rachid; Khoukhi, Fatima El

doi:10.18517/ijaseit.7.2.1871

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…However, such technologies were not able to replace the traditional classroom in which teachers typically consider factors that affect the learning of each student [1]. Therefore, researchers have been studying the adaptation element to be included in CAL system so that the material and the learning session can be personalized to entertain students' heterogeneity [2]. Particularly, Intelligent Tutoring System (ITS) and Adaptive Educational Hypermedia System (AEHS) are then designed under the paradigm of Adaptive Learning System (ALS) in order to cater the issue of personalization [3].…”

Section: Introductionmentioning

confidence: 99%

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Idris

Hashim

Samsudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

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In order to support personalized learning, an adaptive learning system should have a capability to provide each student with a suitable learning material regarding his profile. However, the issue of student varieties in acquiring every Domain Knowledge Concept (DKC), and a range of DKC important variations in a particular learning material produced a complex dependency that causes a difficulty in the learning material selection process. Existing rule-based learning material selection approach requires the definition of a huge set adaptation rules. However, this approach usually results in inaccurate and incorrect selection due to the inconsistent, insufficient and confluence of the defined rules. Consequently, the process of learning material selection is hard to be algorithmized, therefore, intelligent methods are applied to handle the complexity challenges. This research proposes a significance weight approach that represents the complex dependency of learning material selection problem to substitute the rules definition in the selection process. In addition, this research proposes an intelligent learning model that combines unsupervised and supervised machine learning techniques to accurately select the learning material for a particular student adaptively. The unsupervised machine learning technique is vital in obtaining a learning material classification and labelling based on the proposed significance weight. Meanwhile, the supervised machine learning technique, the Multilayer Perceptron Artificial Neural Networks conducts the adaptation process that will assign the student to suitable learning materials regarding his performance upon specific DKC. With 98% achievement of classification accuracies, this model can be considered as highly accurate in selecting a correct and suitable learning material based on student's domain knowledge level.

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Section: Introductionmentioning

confidence: 99%

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Idris

Hashim

Samsudin

et al. 2017

International Journal on Advanced Science, Engineering and Information Technology

View full text Add to dashboard Cite

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Dynamic Multi Agent System for Revising E-Learning Content Materia

Elghibari¹,

Elouahbi²,

Khoukhi³

2019

Turkish Online Journal of Distance Education

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The growth of the information and communication technologies has led to the appearance of new concepts, approach and disciplines. For learners, an e-learning system constitutes rich window to the knowledge. It presents a varied training, including different content material format (video, text, interactive content...) and diverse methods. In order to keep learners’ attention, e-learning system must provide good content's quality, including revised material and updated methods. In this perspective, we have implemented multi-agent system composed of three sort of agents ensuring a permanent revision to the e-learning content. The first one is called Checker Agent (CA). It checks the educational resources, and detects the outdated ones so as to be treated. The second agent is named Search Agent (SA). The task of this one is to look for recent contents and new teaching methods. Whereas, the third agent is called Updater Agent (UA). Its function consists on inserting the retrieved updates corresponding to each content. The communication between these agents is ensured by an XML files. In this paper, we have proposed an implementation of the first part of our system. Namely, the checking process of e-learning curriculum by implementing the CA algorithm. And the integration process by implementing the UA algorithm. As result, the tests and experimentations done in this context have proved the effectiveness of the proposed solution, and revealed positive results both in term of learning process and learners’ feedback.

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An Automatic Updating Process to Control The E-learning Courseware

Cited by 2 publications

References 14 publications

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Intelligent Learning Model Based On Significant Weight Of Domain Knowledge Concept For Adaptive E-Learning

Dynamic Multi Agent System for Revising E-Learning Content Materia

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