Ontology-based model for learning object metadata

Kalogeraki, Eleni-Maria; Troussas, Christos; Apostolou, Dimitris; Virvou, Maria; Panayiotopoulos, Themis

doi:10.1109/iisa.2016.7785383

Cited by 9 publications

(7 citation statements)

References 3 publications

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“…One of the main efficient overviews was in solution, a teach that is especially put resources into prove based research. This all the more as of late enlivened the exchange of the requirement for, and idea of, a precise overview to the area of data frameworks [2] and, in parallel, programming building [3]. Pickard et al [4] examine the need to total the aftereffects of experimental essential examinations to frame an optional report.…”

Section: Relative Workmentioning

confidence: 99%

“…Competitor LOs experience the ill effects of: (a) no or little semantics/explanation, (b) assortment of granularity, and (c) no methods for sticking them together in versatile request to make an intelligible course. Such learning materials can barely fit together [2] in a sensible learning way in light of their distinctive measures. For example, a LO may not fit with another LO straightforwardly, as a result of various metadata principles or diverse learning objects norms or conflicting planned learning results and wanted psychological level.…”

Section: Cloud E-learningmentioning

confidence: 99%

“…E-LEARNING (electronic learning) is a dynamic field in the connected software engineering space, advanced by late data innovation developments. A more typical term is Technology Enhanced Learning (TEL) [1]; recently rose advancements have prompted new types of TEL, for example, m-learning (portable taking in), an administration that ordinarily joins versatile and remote advances to give learning instruments and instructive assets, making lessons accessible and available to anybody wherever through cell phones [2]. Another case of TEL is vlearning (virtual getting the hang of), giving rich learning assets in an extraordinary, effectively open virtual condition [3].…”

Section: Introductionmentioning

confidence: 99%

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A Systematic Mapping Study of Cloud E-Learning Computing for Education

Akram¹,

Kumar²

2018

IJCA

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This paper examines the cloud computing for education (CCE) literature, and analyzes if the research is developing scientifically with adequate empirical validation. All aspects of empirical investigations covered in the literature are shown as weak, hence, the necessary scientific development of CCE requires extending its scope of interest, and involving scholars synergistically to create and maintain a "common research agenda". Differences are found across geographic areas in applying CCE infrastructure and technologies in educational institutions; few studies address CCE's impact on pedagogic processes. The scope of interest in CCE is only partially covered; with empirical research being very shallow. Suggestions are made for more effective research on concerning the production and use of content.

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Section: Relative Workmentioning

confidence: 99%

Section: Cloud E-learningmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Systematic Mapping Study of Cloud E-Learning Computing for Education

Akram¹,

Kumar²

2018

IJCA

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“…One of the areas where ontologies can be used is education. Many ontologies [5,6,7,8] related to learning objects are being developed in the field of education, each day creating a wider use area for themselves.…”

Section: Related Workmentioning

confidence: 99%

Using Domain Ontology for Learning Objects

Gültepe¹,

Balgabayeva²

2018

IJCA

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Learning objects are one of the innovations in domain of teaching technology. Shared and reusable learning objects are based on the understanding that content should be designed in a modular way rather than designed as a single integrated software. Learning objects are separate and independent objects, allowing each individual to create lessons in accordance with his or her own learning style or method, enabling the realization of customized learning. Since learning objects have descriptive information (metadata), they can be easily searched, so that they can access the learning content on time. The metadata definitions are made in the XML file. For this reason, learning objects can be shared with the use of XML, while the reuse of learning objects cannot be provided because XML is insufficient in the semantic definition of learning objects. Semantic Web (Web 3.0) technologies can be used to produce workable and interpretable web pages. Ontologies are being developed with the use of these technologies. Thanks to the ontology, intelligent learning environments can be developed to access the learning objects that are distributed on the web about each learning acquisition. In this study, according to the curriculum of Computer Engineering, ComputerEngineringCurriculum learning object ontology was defined. The defined learning object ontology provides better sharing and reusability of learning objects. Protégé ontology development editor was used for this purpose. This paper shows that learning environments developed using semantic web technologies and ontologies can offer intelligent solutions for individualized instruction and rapid access to accurate instructional content on the web.

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“…O uso de ontologias para representar modelos tem sido bastante explorado na área educacional. Kalogeraki et al (2016) descreve a diĄculdade dos repositórios de OA em realizar uma comunicação entre eles (por conta de problemas de interoperabilidade) e como técnicas de Web Semântica, através da ontologia, são promissoras para o reúso e compartilhamento de OA. Nesse sentido, é possível avaliar a relação semântica entre OA e utilizar os resultados para a criação de um sistema de gerenciamento de OA para educação a distância.…”

Section: Web Semântica E Ontologiasunclassified

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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Ontology-based model for learning object metadata

Cited by 9 publications

References 3 publications

A Systematic Mapping Study of Cloud E-Learning Computing for Education

A Systematic Mapping Study of Cloud E-Learning Computing for Education

Using Domain Ontology for Learning Objects

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

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