A learning style-based Ontology Matching to enhance learning resources recommendation

Aissaoui, Ouafae El; Oughdir, Lahcen

doi:10.1109/iraset48871.2020.9092142

Cited by 23 publications

(11 citation statements)

References 12 publications

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“…These tables are important to guide the process of content adaptation. Nafea et al (2019) and Aissaoui and Oughdir (2020) also present a mapping between Learning Objects and Learning Styles for content personalization. The first work explores similarity measures to recommend Learning Objects according to the student's Learning Style.…”

Section: Related Workmentioning

confidence: 99%

Relationship Between Learning Styles and Learning Objects

Assis

Rodrigues

Andrade

et al. 2022

International Journal of Distance Education Technologies

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The automation of learning object recommendation and learning styles detection processes has attracted the interest of many researchers. Some works consider Learning Styles to recommend Learning Objects. On the other hand, other works automatically detect Learning Styles, analyzing the behavior of students in Intelligent Tutorial Systems in relation to the use of Learning Objects. Taking into account that advances in this field of research have been constantly presented in recent years, this paper analyzes the results of works discovered through a Systematic Literature Review. The main objective was to discover and document the relationships between Learning Styles and Learning Objects considered by researchers, in order to provide accurate content recommendations. The results show inconsistencies in the process, indicating that more and more in-depth research is still needed to allow a more accurate understanding of how to consider Learning Styles in the Learning Object recommendation process.

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Section: Related Workmentioning

confidence: 99%

Relationship Between Learning Styles and Learning Objects

Assis

Rodrigues

Andrade

et al. 2022

International Journal of Distance Education Technologies

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“…In this respect, recommendation systems can manage multiple resources within a learning environment and assist in guiding learners towards a more efficient learning process [5][6]. Many researchers addressed recommendation systems in e-learning [7][8][9]. Techniques and approaches covered are also varied, from personalized approaches, collaborative filtering approaches to hybrid approaches [10][11][12].…”

Section: Introductionmentioning

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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“…Ainda com relação a técnicas computacionais e FBC, aplicações com Web Semântica costumam ser combinadas no processo de recomendação (RODRÍGUEZ et al, 2017;MORILLO-PALACIOS;GUTIÉRREZ-CÁRDENAS, 2020;AISSAOUI;OUGHDIR, 2020). No caso do trabalho de Rodríguez et al (2017) (RODRÍGUEZ et al, 2017;DOJA et al, 2020).…”

Section: Trabalhos Correlatosunclassified

“…Além da técnica de similaridade, já previamente esperada, foi possível encontrar aplicações com uso de Web Semântica (RODRÍGUEZ et al, 2017;MORILLO-PALACIOS;GUTIÉRREZ-CÁRDENAS, 2020;AISSAOUI;OUGHDIR, 2020). No caso do trabalho de Rodríguez et al (2017) as regras de inferência OWL foram utilizadas para realização da Ąltragem.…”

Section: A22 Execuçãounclassified

“…Eles são utilizados tanto para traçar perĄl de preferências de estudantes, quanto para modelar OA, isto é, indicar para qual perĄl aquele OA deve ser recomendado. O FSLSM tem sido o mais predominante dentre os trabalhos que utilizam EA e FBC (HASSAN;HAMADA, 2017;NAFEA;SIEWE;HE, 2019; JORDÁN et al, 2020;AISSAOUI;OUGHDIR, 2020). Há também pesquisas que utilizam os EA do tipo VARK como um dos atributos para modelagem do aprendiz…”

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Uma abordagem híbrida apoiada por algoritmo bioinspirado e tecnologias de web semântica para recomendação personalizada de objetos de aprendizagem

Júnior¹,

Xavier²

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Sem me preocupar com ordem de importância, pois todos contribuíram para o meu trabalho em algum momento, começo agradecendo meus orientadores e ex orientadoras durante esses anos, sendo eles/as: Ana Paula Laboissière Ambrósio (in memorian), Fabiano Azevedo Dorça, Márcia Aparecida Fernandes e Rafael Dias Araújo.Agradeço os amigos que já caminhavam comigo mesmo antes do doutorado e que nunca hesitaram em me dar forças sempre que precisei. Aqui destaco meus/minhas amigos/as de formação e de proĄssão:

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A learning style-based Ontology Matching to enhance learning resources recommendation

Cited by 23 publications

References 12 publications

Relationship Between Learning Styles and Learning Objects

Relationship Between Learning Styles and Learning Objects

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

Uma abordagem híbrida apoiada por algoritmo bioinspirado e tecnologias de web semântica para recomendação personalizada de objetos de aprendizagem

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