E-learning Recommendation System Based on Cloud Computing

Rahhali, Mounia; Oughdir, Lahcen; Jedidi, Youssef; Lahmadi, Youssef; Khattabi, Mohammed Zakariae El

doi:10.1007/978-981-33-6893-4_9

Cited by 15 publications

(13 citation statements)

References 14 publications

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“…Faced with several courses and resources, it is tricky for students in the e-learning environment to determine associated learning resources. Besides, due to the diversity in the background; various students have various learning demands [2]. Accordingly, it is essential to improve recommender systems to assist learners in choosing courses, resources, or learning materials in E-learning [21].…”

Section: Advantages Of E-learning During Pandemic Situationmentioning

confidence: 99%

“…Presently education is increasingly correlated with ICT (Information and communication technology), so educational institutions have a high interest in servers, storage, and software [1]. Furthermore, they need access to the necessary resources to enhance the performance and reduced the cost of production education for their learners [2]. In addition, most educational institutions utilize big amounts of information in both structured and unstructured types.…”

Section: Introductionmentioning

confidence: 99%

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E-Learning Recommendation System for Big Data Based on Cloud Computing

Rahhali

Oughdir

Jedidi³

2021

Int. J. Emerg. Technol. Learn.

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In educational institutions, E-learning has been known as a successful technology for enhancing performance, concentration, and thus providing higher academic success. Nevertheless, the conventional system for executing research work and selecting courses is a time-consuming and unexciting practice, that not only directly impacts the students ’ academic achievement but also impacts the learning experience of students. In addition to that, there is an enormous number of various kinds of data in the E-Learning domain both structured and unstructured, and the academic establishments attempt to manage and understand big complicated data sets. To fix this problem, this paper proposes a model of an E-learning recommendation system that will suggest and encourage the learner in choosing the courses according to their needs. This system used big data tools such as Hadoop and Spark to enhance data collection, storage, analysis, processing, optimization, and visualization, furthermore based on cloud computing infrastructure and especially Google cloud services.

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Section: Advantages Of E-learning During Pandemic Situationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

E-Learning Recommendation System for Big Data Based on Cloud Computing

Rahhali

Oughdir

Jedidi³

2021

Int. J. Emerg. Technol. Learn.

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“…Understanding and adapting to these preferences is essential for businesses to thrive in a competitive market, as consumers now have more information, options, and influence than ever before. Consequently, staying attuned to evolving consumer preferences is a fundamental strategy for success in the modern marketplace [8]. Consumer preferences in folk music are deeply rooted in culture, tradition, and personal experiences.…”

Section: Introductionmentioning

confidence: 99%

Consumer Preference Measurement of Folk Culture Based on Confidence Rule Base Model

Yuan Xie

2024

jes

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Deep learning algorithms can help uncover patterns, make predictions, or generate new content related to folk culture, thus bridging the gap between heritage and advanced technology. The confidence model in a cloud context refers to a system or approach used to assess the reliability, security, or performance of cloud services. It might involve factors such as service uptime, data security, scalability, and compliance with industry standards. In this paper focused on the dynamic landscape of consumer preferences within folk music through cloud-based technologies integrated with deep learning. Folk music, with its rich cultural diversity and historical significance, presents a unique context for investigating the intricacies of consumer taste. The proposed model uses the "Ranking" based deep learning within cloud-based resources to predict and classify consumer preferences effectively. With the integration of the cloud confidence model ranking is implemented for the estimation of tracks in folk music. The estimated tracks are evaluated and stored in the cloud environment based on the preferences of the customers. The classification of the tracks and consumer preferences are ranked with the cloud model features. The simulation results demonstrated that the ranking of tracks effectively improves consumer preferences with the cloud confidence model in folk music. The results enhancing personalized experiences and facilitating informed decision-making for businesses and cultural institutions operating in the rich and diverse landscape of folk culture.

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“…Educational institutions regard online education as a successful technology that improves learning results, attention, and thus learning effects. Rahhali et al [18] proposed a model for online education resource recommendation system, which suggest and encourage learners to choose courses according to their needs. The system relies on big data tools like Hadoop and Spark to better collect, store, analyze, process, optimize, and visualize data, and makes full use of cloud computing infrastructure, especially Google Cloud Services.…”

Section: Introductionmentioning

confidence: 99%

Big Data-Assisted Recommendation of Personalized Learning Resources and Teaching Decision Support

Huiji¹

2022

Int. J. Emerg. Technol. Learn.

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The evaluation of personalized learning features can perceive the features of learning behaviors intelligently, and provide direct, reliable decision support for promoting personalized learning resources (PLRs). The current research has an urgent need to overcome several problems of PLR recommendation: the recommended PLRs fall short of demand, the learning behaviors are not analyzed dynamically, and the learning intentions are not predicted well. To solve these problems, this paper explores the big data-assisted recommendation of PLRs and teaching decision support. The education of spoken and written languages was taken as an example during the exploration. The authors detailed the flow of the proposed algorithm, and proved its effectiveness through experiments.

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E-learning Recommendation System Based on Cloud Computing

Cited by 15 publications

References 14 publications

E-Learning Recommendation System for Big Data Based on Cloud Computing

E-Learning Recommendation System for Big Data Based on Cloud Computing

Consumer Preference Measurement of Folk Culture Based on Confidence Rule Base Model

Big Data-Assisted Recommendation of Personalized Learning Resources and Teaching Decision Support

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