A fog based recommendation system for promoting the performance of E-Learning environments

Ibrahim, Taghreed S.; Saleh, Ahmed I.; Elgaml, Nehad; Abdelsalam, Mohamed M.

doi:10.1016/j.compeleceng.2020.106791

Cited by 27 publications

(16 citation statements)

References 19 publications

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“…Used shortest path algorithm Hwang et al( 2020a ) Hybrid (Fuzzy Logic Based, Rule-Based) Learners' affective and cognitive factors Through Ontology Model serving the learner's learning behaviors, the expert system determines the learner's affective states, and the learner's cognitive status is registered. To suggest pre-classified LOs based on the above criteria, a set of fuzzy inference rules is used Ibrahim et al ( 2020 ) Hybrid (Ontology Model, Fuzzy Logic Based) Learning goals, Learner Interests The contents' relevant class is chosen based on the frequency concept and weight concept for a requested topic in the class identification module. Later, a subclass for the requested matter is identified with the association rule mining technique's help.…”

Section: Analysis Of Literature On Content Recommender Systemsmentioning

confidence: 99%

“…,Xiao et al (2018),Deng et al (2018),Zhang et al (2019),Bhaskaran and Santhi (2019),Nafea et al (2019),Riyahi and Sohrabi (2020),Ibrahim et al (2020),Venkatesh and Sathyalakshmi (2020),Jagadeesan and Subbiah (2020, Murad et al (2020),Anuradha et al (2020), and Nabizadeh et al (2020) Learner Score Senthilnayaki et al (2015), Wan and Niu (2016), Benhamdi et al (2017), Christudas et al (2018), Wan and Niu (2018), Dwivedi et al, (2018, Aeiad and Meziane (2019), Segal et al (2019), Vanitha and Krishnan (2019), Sarwar et al (2019), Jagadeesan and Subbiah (2020), Hwang et al(2020a), and Nabizadeh et al (2020)Usage ofLO Wan and Niu (2016) andWan and Niu (2018) Run Time,Christudas et al (2018),Klašnja- Milićević et al, (2018a, andBhaskaran and Santhi (2019) Learner SatisfactionTarus et al (2017), Zhu (2018) Christudas et al(2018,Wan and Niu (2018),Rahman and Abdullah (2018),Klašnja-Milićević et al, (2018a,Nafea et al (2019),Kouis et al (2020),Shi et al (2020), andNabizadeh et al (2020) …”

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confidence: 99%

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A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Renumol

2021

J. Comput. Educ.

115

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In personalized learning, each student gets a customized learning plan according to their pace of learning, instructional preferences, learning objects, etc. Hence the content recommender system in Personalized Learning Environment (PLE) should adapt to learner attributes and suggest appropriate learning resources to aid the learning process and improve the learning outcomes. This systematic literature review aims to analyze and summarize the studies on learning content recommenders in adaptive and personalized learning environments from 2015 to 2020. The publications were searched using proper keywords and filtered using the inclusion and exclusion criteria, which resulted in 52 publications. This paper summarizes the recent trends in research on different aspects of the recommender systems, such as learner attributes, recommendation methods, evaluation metrics, and the usability tests used by the researchers. It is observed that cognitive aspects of learners like learning style, preferences, knowledge level, etc., are used by most studies than non-cognitive aspects as social tags or trust. In most cases, recommendation engines are a hybrid of collaborative filtering, content-based filtering, ontological approaches, etc. All models were evaluated for the correctness of the prediction done, and a few studies have also done evaluations based on learner satisfaction or usability.

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Section: Analysis Of Literature On Content Recommender Systemsmentioning

confidence: 99%

mentioning

confidence: 99%

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Renumol

2021

J. Comput. Educ.

115

View full text Add to dashboard Cite

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“…• A fog-based recommendation system [36] The proposed recommendation system contains three modules: a) Class identification module: calculating the correlation factor b) Subclass identification module c) Correspondence module: matching subclasses to available contents…”

Section: • a Hybrid System Based On Ontology [34]mentioning

confidence: 99%

Towards an Evolution of E-Learning Recommendation Systems: From 2000 to Nowadays

Souabi¹,

Retbi²,

Idrissi³

et al. 2021

Int. J. Emerg. Technol. Learn.

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In e-learning, recommendation systems have proven to be highly efficient for improving learners' performance and knowledge. They can manage the different pedagogical resources and simplify the workload for the instructor and learners as well. Throughout the years, recommendation systems in e-learning have wit-nessed a major evolution since the 2000s. Several aspects have been developed, including techniques involved, test data (...). In this respect, this paper analyses the evolution of recommendation systems in e-learning since 2000 with a focus on the evolution sides. It furthermore addresses areas not fully addressed to date. A set of recommendation systems is identified and then analysed in order to define techniques used, as well as algorithms deployed.

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“…It can overcome the problem of information overload in fog computing and help in generating personalized recommendations system performance. In the same manner, a fog-based recommender system which helps to bridge the gap between the cloud and end-devices is proposed in [240]. This system has been used to improve the performance of the E-Learning environments.…”

Section: Edge/fog Recommender Systemsmentioning

confidence: 99%

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

Himeur¹,

Alsalemi²,

Al‐Kababji³

et al. 2021

Preprint

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Recommender systems have significantly developed in recent years in parallel with the witnessed advancements in both internet of things (IoT) and artificial intelligence (AI) technologies. Accordingly, as a consequence of IoT and AI, multiple forms of data are incorporated in these systems, e.g. social, implicit, local and personal information, which can help in improving recommender systems' performance and widen their applicability to traverse different disciplines. On the other side, energy efficiency in the building sector is becoming a hot research topic, in which recommender systems play a major role by promoting energy saving behavior and reducing carbon emissions. However, the deployment of the recommendation frameworks in buildings still needs more investigations to identify the current challenges and issues, where their solutions are the keys to enable the pervasiveness of research findings, and therefore, ensure a large-scale adoption of this technology. Accordingly, this paper presents, to the best of the authors' knowledge, the first timely and comprehensive reference for energy-efficiency recommendation systems through (i) surveying existing recommender systems for energy saving in buildings; (ii) discussing their evolution; (iii) providing an original taxonomy of these systems based on specified criteria, including the nature of the recommender engine, its objective, computing platforms, evaluation metrics and incentive measures; and (iv) conducting an in-depth, critical analysis to identify their limitations and unsolved issues. The derived challenges and areas of future implementation could effectively guide the energy research community to improve the energy-efficiency in buildings and reduce the cost of developed recommender systems-based solutions.

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A fog based recommendation system for promoting the performance of E-Learning environments

Cited by 27 publications

References 19 publications

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

A systematic literature review on adaptive content recommenders in personalized learning environments from 2015 to 2020

Towards an Evolution of E-Learning Recommendation Systems: From 2000 to Nowadays

A survey of recommender systems for energy efficiency in buildings: Principles, challenges and prospects

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