Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Sabeima, Massra; Lamolle, Myriam; Nanne, Mohamedade Farouk

doi:10.14569/ijacsa.2022.0130803

Cited by 3 publications

(2 citation statements)

References 27 publications

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“…There has been increasing research into using recommendations in the education industry [166]. Several studies have focused on the use of a recommendation system to provide personalized and tailored educational experiences through the use of computerbased learning [167][168][169]. Such research has highlighted the potential of recommendations to increase student engagement, improve student performance, and reduce the need for teachers to provide instruction.…”

Section: Educationmentioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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Big data is a rapidly growing field, and new developments are constantly emerging to address various challenges. One such development is the use of federated learning for recommendation systems (FRSs). An FRS provides a way to protect user privacy by training recommendation models using intermediate parameters instead of real user data. This approach allows for cooperation between data platforms while still complying with privacy regulations. In this paper, we explored the current state of research on FRSs, highlighting existing research issues and possible solutions. Specifically, we looked at how FRSs can be used to protect user privacy while still allowing organizations to benefit from the data they share. Additionally, we examined potential applications of FRSs in the context of big data, exploring how these systems can be used to facilitate secure data sharing and collaboration. Finally, we discuss the challenges associated with developing and deploying FRSs in the real world and how these challenges can be addressed.

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Section: Educationmentioning

confidence: 99%

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

et al. 2023

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“…A new architecture for piloJng and customizing adapJve learning paths was introduced in Sabeima, Massra and Lamolle, Myriam & Nanne, Mohamedade (2022). considering users' profile, training domain and the available learning resources.…”

Section: Figure 3: Vark Learning Stylesmentioning

confidence: 99%

Intelligent Adaptive E-Learning Systems: Current Approaches, Architectures, and Applications

Aljuboori,

Al-lawati

2023

ECEL

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Considering the weakness of traditional e-learning, a multiple of e-learning systems attempt to get individualization into the process of learning by offering learner-centered instruction, the adaptive E-learning system(AES)is seen as one of the more famous models. An AES can tailor its response to different circumstances. Various types and platforms in E-Learning systems have been discussed and explained such as blended, adaptive, and educational e-learning. Architectures, functions, and challenges also illustrated in details in this paper. Mostly focused on the Adaptive e-learning concept and importance for the learners. AES concentrates on adaptively delivering learning materials. This paper discusses some studies that approved the importance of adaption in the e-learning system in the years (2018-2023), and introduced some challenges faced by this system.

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How AI Works: Reconfiguring Lifelong Learning

Palenski,

Hills,

Unnikrishnan

et al. 2024

Postdigit Sci Educ

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Lifelong learning is a current policy focus in many countries, with AI technologies promoted as both the motivation for the need for lifelong learning (due to its assumed role in social change) and as an important way to ‘deliver’ learning across the life course. Such policies tend to be instrumental and technologically deterministic, and there is a need to properly theorize the relationships between AI and lifelong learning to better inform policy and practice. In this paper, we examine the ways that academic communities conceptualize AI and lifelong learning, based on a thematic analysis of existing academic literature in contexts beyond formal education. We identify three groups of research, which vary according to their engagement with theories of learning and AI technology and how AI ‘works’. In group 1 (working AI), AI is assumed to contribute to increased efficiency of humans and learning; in group 2 (working with AI), AI is implemented and conceptualized as a peer or colleague; and in group 3 (reconfiguring AI), AI is viewed as part of a wider reconfiguration of humans and their contexts. This latter group, though least well represented in the literature, holds promise in advancing a postdigital research agenda that focuses not solely on how AI works to increase efficiency, but how people are increasingly working, learning, and living with AI, thus moving beyond exclusively instrumental, economic, and technologically deterministic concerns.

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Towards Personalized Adaptive Learning in e-Learning Recommender Systems

Cited by 3 publications

References 27 publications

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

A Comprehensive Survey on Privacy-Preserving Techniques in Federated Recommendation Systems

Intelligent Adaptive E-Learning Systems: Current Approaches, Architectures, and Applications

How AI Works: Reconfiguring Lifelong Learning

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