A Personalized E-Learning Based on Recommender System

Bourkoukou, Outmane; Bachari, Essaid El; Adnani, Mohamed El

doi:10.18178/ijlt.2.2.99-103

Cited by 29 publications

(23 citation statements)

References 17 publications

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“…They were satisfied to learn with the preferred learner's profile and willing to use the system in the future. The results seem to support earlier studies which concluded that using learner profile matching with the learners' psychology is helpful to students in enhancing both learning efficacy and efficiency [8,11,29,30].…”

Section: Resultssupporting

confidence: 87%

“…In this regard, several studies have been done to show if the personalized learning according to the learner profile can improve and enhance the performance of learning process. Results seem to be mixed [8,23,24], but most authors agree that taking into account the learning style in e-learning system could help learners to achieve an effective and efficient learning [11,24,29,30].…”

Section: Related Workmentioning

confidence: 99%

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Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Bourkoukou

Bachari

2018

JOIV : Int. J. Inform. Visualization

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Personalized courseware authoring based on recommender system, which is the process of automatic learning objects selecting and sequencing, is recognized as one of the most interesting research field in intelligent web-based education. Since the learner’s profile of each learner is different from one to another, we must fit learning to the different needs of learners. In fact from the knowledge of the learner’s profile, it is easier to recommend a suitable set of learning objects to enhance the learning process. In this paper we describe a new adaptive learning system-LearnFitII, which can automatically adapt to the dynamic preferences of learners. This system recognizes different patterns of learning style and learners’ habits through testing the psychological model of learners and mining their server logs. Firstly, the device proposed a personalized learning scenario to deal with the cold start problem by using the Felder and Silverman’s model. Next, it analyzes the habits and the preferences of the learners through mining the information about learners’ actions and interactions. Finally, the learning scenario is revisited and updated using hybrid recommender system based on K-Nearest Neighbors and association rule mining algorithms. The results of the system tested in real environments show that considering the learner’s preferences increases learning quality and satisfies the learner.

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Section: Resultssupporting

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Bourkoukou

Bachari

2018

JOIV : Int. J. Inform. Visualization

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“…If it is added at that moment, the user might get a clue for specific possible reworking. In conclusion, there are circumstances where the effectiveness of the explanations can only judge if they have implemented in reality [12].…”

Section: Problems and Solutionmentioning

confidence: 96%

Enhanced e-Learning Experience using Case based Reasoning Methodology

Shekapure¹,

Dipti²

2019

IJACSA

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In recent year's improvement in innovation includes new limits for verifying data that will incite essential changes in eLearning. The user can see e-learning material subject to the reference given to them and select the best approach to see the resources. This proposed system addresses retrieval, reuse, revise and retain phases of CBR. For building personalized e-Learning, this work identifies different feature set such as learning style, learning object, knowledge level, and problem list. For constructing this model used case-based reasoning along with a k-nearest neighbour. Role of the Knearest neighbour method is to identify the perfect k factor for better analysis for calculation of accurate retrieval process. There is further addition of new cases based on the simulation of new user history limit to a certain threshold value. This model acquires dynamically incremental dataset for classification. Further, there is time and accuracy comparison on dataset done by K-nearest neighbour, decision tree and support vector machine. Eventually, eLearning spares time, upgrades the learning knowledge and gives scholarly achievement.

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“…Adaptive presentation: content concepts are changing according to the student's needs. [10] proposed a personalized e-learning recommender system, called PERS, based on collaborative filtering recommendation to propose adaptive class materials that match student's needs based on group preferences. The cold start problem is solved using a static questionnaire per each new student base on Index Learning Style Questionnaire (ILSQ) to create initial model and initial recommendation.…”

Section: Content Modelmentioning

confidence: 99%

A Framework for Adaptive Personalized E-learning Recommender Systems

Moharm¹

2019

IJIIS

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With the undergoing technological revolution in education, adapting recommender systems to the personalized elearning is an emerging topic in the education sector. Detecting the student model offers a potential to recommend a learning material that is adequate to the student progress. Accordingly, the learning objects and hypermedia can be adapted to each individual student to meet the personalized learning needs. This paper proposes a framework for applying recommender systems in personalized e-learning domain. Furthermore, the recommender system previous examples, opportunities, and associated challenges are discussed.

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A Personalized E-Learning Based on Recommender System

Cited by 29 publications

References 17 publications

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Toward a Hybrid Recommender System for E-learning Personnalization Based on Data Mining Techniques

Enhanced e-Learning Experience using Case based Reasoning Methodology

A Framework for Adaptive Personalized E-learning Recommender Systems

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