Semantic Collaborative Filtering Recommender System Using CNNs

Zaremarjal, Ashkan Yeganeh; Yiltas-Kaplan, Derya

doi:10.1109/iceee52452.2021.9415931

Cited by 3 publications

(1 citation statement)

References 29 publications

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“…To enhance learning resource recommendations efficiency, more recent systems implement deep learning [67,169,188], machine learning [156], AI [28,290] or hybrid methods [199,208,256] to implement CF for choosing the more efficient learning resource to recommend. The recommendation is based on the influence sets of the e-learning group's behaviour [171], learner's learning activities and performances [123] or person correlation and similarity [133,143].…”

Section: Rssmentioning

confidence: 99%

Personalisation methods in e‐learning‐A literature review

Mikić¹,

Ilić²,

Kopanja³

et al. 2022

Comp Applic In Engineering

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Over the years, personalisation in e‐learning has evolved as a promising paradigm for matching learners' specific requirements. Although many literature reviews on personalised e‐learning have been conducted, there are still voids and limitations in the comprehensive literature survey regarding how personalisation might occur in e‐learning environments and benefit the teaching and learning process. In addition, no design guidelines, frameworks, or tools to guide its implementation exist, so practice can vary widely. The principal purpose of this literature review is to examine the subject, provide insights into the use of personalised techniques and systems in e‐learning, and investigate its impact on the engagement and success of the students. This study applies methods of precise selection criteria to determine which of the selected publications have the strongest interconnection and relevance with the topic of e‐learning personalisation. It also categorises the personalisation techniques employed by each identified system and highlights new perspectives and advances in the area.

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