Intelligent e-learning system model for maintenance of updates courses

Elghibari, Fatiha; Elouahbi, Rachid; Elkhoukhi, Fatima; Chehbi, Sanae; Kamsa, Imane

doi:10.1109/ithet.2015.7218028

Cited by 8 publications

(3 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this context, a simulation approach of removing ambiguity context of a difficult topic to understand through the virtual interaction is implemented. This approach aims at helping learners understand a subject by a reciprocal exchange between a set of virtual learners, including the target learner .In order to achieve this objective, we have adopted the intelligent agents as a key solution to our modelling regarding the benefits they offer [25][26]. We have associated each learner to autonomous agents group which act and react automatically and intervene on the scheduled time in the system.…”

Section: Methodsmentioning

confidence: 99%

Interaction in Online System is A Favor Key for Learners’ Success

Kamsa

Elouahbi

Khoukhi

2017

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

View full text Add to dashboard Cite

Nowadays, the online interaction has become more necessary and vital in terms of both socio-cognitive and socio-affective levels. It fully plays an important role in the acquisition of knowledge in online learning. However, the way in which these interactions take place remain little regulated and less efficient. Recognizing this, we have developed an approach used to automatically schedule the interactions in the learning process of learners. These interactions will easily help learners to assimilate the obscure concepts and quite difficult to understand. Our goal is achieved basing on intelligent agents modeling of virtual learners. These agents enter into discussion with learners and are widely involved in clarifying these concepts. To affirm the importance of our study in e-learning, we have given a questionnaire to the learners of the Master in French literature. This latter has confirmed that the majority of the learners choose to collaborate and interact with each other to come over the blockage points in their learning process. Furthermore, the empirical result has shown that our approach contributes effectively in online learning. A significant increase in learner results is well noticed.

show abstract

Section: Methodsmentioning

confidence: 99%

Interaction in Online System is A Favor Key for Learners’ Success

Kamsa

Elouahbi

Khoukhi

2017

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

View full text Add to dashboard Cite

show abstract

“…Sometimes, learners face problems due to some anomalies in the proposed content itself. Namely, bad construction, lack of some definitions or reminders that help to quick understanding of some concepts [10]. Also, the absence of examples facilitating the assimilation of the objective of some complicated notions.…”

Section: Introductionmentioning

confidence: 99%

Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

Elghibari

Elouahbi

Khoukhi

2018

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

View full text Add to dashboard Cite

E-learning adaptation has become the most important method that facilitates access to the appropriate content. Adaptive approaches consist of reducing the problems of incompatibilities between learner's cognitive abilities and educational content's difficulties. In some cases, the adapted curriculum cannot meet learner's skills completely seen its incoherent structure, its unsuitable methodologies and sometimes its complexity. Therefore, we need to measure the convenience of the content material to improve it and ensure learners' satisfaction. In other words, it is necessary to estimate its appropriateness to each learner. That is why; we have proceeded by using decision tree (DT) algorithm which is a supervised data mining method. It helps to predict the convenience of the proposed content material for learners. Our system consists of classifying learning material into two classes: "good" if it is convenient, and "anomaly" if not. To achieve that, we have used an intelligent agent called Classifier Agent (CLA). It tracks learner's behavior by collecting a set of attributes like score, learning time, and number of attempts, feedback and interactions with the tutor. Then, he calculates the predictive attribute by using the (DT) algorithm. The finding algorithm shows that the score is the most crucial indicator gives us more information about the conformity of curriculum to learners, followed by learning time, feedback and number of attempts.

show abstract

“…The lack of updates courses is one of the major problems that can block learners. In term of content, students will be demotivated in face of an old course version, including content using outdated tools and containing non-updated information [8]. And in term of design and technologies, the system will disappear quickly, because in general, the web sites have a high rate of birth and death in the cyberspace.…”

Section: Introductionmentioning

confidence: 99%

An Automatic Updating Process to Control The E-learning Courseware

Elghibari¹,

Elouahbi²,

Khoukhi³

2017

International Journal on Advanced Science, Engineering and Information Technology

Self Cite

View full text Add to dashboard Cite

updating courses in e-learning systems is considered one of the main factors to ensure a relevant educational content. Basically, providing to the learner a dynamic learning path that contains the latest trends of information and software tools, and also tracking technological development. For this reason, we proposed an automatic mechanism that facilitates the integration of the latest updates in e-learning system in order to keep it to date in one hand, and reduces time and labor devoted for this operation in the other hand. To realize this process, we opted to use intelligent agent technology in order to manage these updates. Namely, Updater Agent (UA) ensuring their introduction in the courseware, and Search Content Agent (SCA) which looks for new ones. In this article, we focused on the contents' integration process by describing the behavior of UA. Then, we will explain life cycle of course, in order to show its different states in the system, and the role of each one to reach the performance of our updating process. Finally, we will describe the software implementation of the UA allowing the management of updates. Namely, tests and experimentation done in this field proved the effectiveness of the proposed solution and revealed a positive results in term of learning process.

show abstract

Intelligent e-learning system model for maintenance of updates courses

Cited by 8 publications

References 8 publications

Interaction in Online System is A Favor Key for Learners’ Success

Interaction in Online System is A Favor Key for Learners’ Success

Data Mining for Detecting E-learning Courses Anomalies: An Application of Decision Tree Algorithm

An Automatic Updating Process to Control The E-learning Courseware

Contact Info

Product

Resources

About