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

Bourkoukou, Outmane; Bachari, Essaid El

doi:10.30630/joiv.2.4.158

Cited by 27 publications

(22 citation statements)

References 20 publications

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“…e outcomes of this study's experiments suggest that using RS to select courses works well. Bourkoukou and Bachari [10] described LearnFitII, an adaptive learning system that can automatically adjust to learners' changing preferences. e goal of this project is to propose a personalized framework that uses a hybrid-based recommendation strategy to provide suitable recommendations and to cope with the cold-start problem, which occurs when there is a lack of knowledge about a new learner.…”

Section: Related Literaturementioning

confidence: 99%

[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

Zheng

2022

Journal of Mathematics

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Differentiated pedagogy is a flexible and organized adaptation of teaching and learning as it argues that students, even those of the same age, have differences in learning readiness, interests, learning style, experiences, and living circumstances. These differences are important in the determination of requirements of their learning and the way of effective learning. In addition, the foundation for effective learning is the sense of community within the classroom, the authentic learning opportunities of using educational equipment, and the connection of the lesson with the experiences and interests of the students. In essence, the support of a teacher guides the pupils to learn to work on their own during a declining guidance policy, to improve their abilities and skills. Thus, the teachers are asked to modify their teaching methods instead of applying a similar way of teaching for all students. The modified teaching style should meet the different levels of readiness of students, the different ways they learn, and their different interests. In support of this specific task for teachers, the current work presents a personalized education system based on hybrid intelligent recommendations. Specifically, a hybrid framework of artificial intelligence is proposed, which focuses on the way to provide targeted recommendations for the implementation of integrated standard lesson plans, which will be the main tool for creating flexible differentiated pedagogical programs that will perfectly meet the personal needs and particularities of each student.

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Section: Related Literaturementioning

confidence: 99%

[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

Zheng

2022

Journal of Mathematics

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“…The purpose of this section is to highlight some work carried out in terms of hybrid systems in e-learning. (Bourkoukou and Bachari, 2018) provide a Learningfitll recommendation system that can be adapted to the learner's different dynamic preferences. The goal is in fact to merge the two aspects of Data Mining: K-nearest neighbors and association rules.…”

Section: Hybrid Recommendation Systemsmentioning

confidence: 99%

Recommendation Systems on E-Learning and Social Learning: A Systematic Review

Souabi¹,

Retbi²,

Idrissi³

et al. 2021

EJEL

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E-learning is renowned as one of the highly effective modalities of learning. Social learning, in turn, is considered to be of major importance as it promotes collaboration between learners. For properly managing learning resources, recommender systems have been implemented in e-learning to enhance learners' experience. Whilst recommender systems are of widespread concern in online learning, it is still unclear to educators how recommender systems can improve the learning process and have a positive impact on learning. This paper seeks to provide an overview of the recommender systems proposed in e-learning between 2007 and the first part of 2021. Out of 100 initially identified publications for the period between 2007 and the first part of 2021, 51 articles were included for final synthesis, according to specific criteria. The descriptive results show that most of the disciplines involved in educational recommender systems papers have approached e-learning in a general way without putting as much emphasis on social learning, and that recommender systems based on explicit feedbacks and ratings were the most frequently used in empirical studies. The synthesis of results presents several recommender systems types in e-learning: (1) Content-based recommender systems, (2) Collaborative-filtering recommender systems, (3) Hybrid recommender systems and (4) Recommender systems based on supervised and unsupervised algorithms. The conclusions reflect on the almost lack of critical reflection on the importance of addressing recommender systems in social learning and social educational networks in particular, especially as social learning has particular requirements, the weak databases size used in some research work, the importance of acknowledging the strengths and weaknesses of each type of recommender system in an educational context and the need for further exploration of implicit feedbacks more than explicit learners’ feedbacks for more accurate recommendations.

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“…One way to personalize education is by using recommender systems that provide useful suggestions for users (books, movies, products, etc.) based on their preferences and their similarity to other students (Bourkoukou and El Bachari, 2018).…”

Section: Challenge Descriptionmentioning

confidence: 99%

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

Injadat,

Moubayed,

Nassif

et al. 2021

Preprint

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The emergence and continued reliance on the Internet and related technologies has resulted in the generation of large amounts of data that can be made available for analyses. However, humans do not possess the cognitive capabilities to understand such large amounts of data. Machine learning (ML) provides a mechanism for humans to process large amounts of data, gain insights about the behavior of the data, and make more informed decision based on the resulting analysis. ML has applications in various fields. This review focuses on some of the fields and applications such as education, healthcare, network security, banking and finance, and social media. Within these fields, there are multiple unique challenges that exist. However, ML can provide solutions to these challenges, as well as create further research opportunities. Accordingly, this work surveys some of the challenges facing the aforementioned fields and presents some of the previous literature works that tackled them. Moreover, it suggests several research opportunities that benefit from the use of ML to address these challenges.

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

Cited by 27 publications

References 20 publications

[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

[Retracted] Personalized Education Based on Hybrid Intelligent Recommendation System

Recommendation Systems on E-Learning and Social Learning: A Systematic Review

Machine Learning Towards Intelligent Systems: Applications, Challenges, and Opportunities

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