Adopting the metadata approach to improve the search and analysis of educational resources for online learning

Valiente, María-Cruz; Sicilia, Miguel–Ángel; García‐Barriocanal, Elena; Rajabi, Enayat

doi:10.1016/j.chb.2014.12.059

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…Uma parte importante a ser considerada para os repositórios de REAs são os metadados relacionados com os recursos armazenados no repositório. Um metadado pode ser traduzido como um dado sobre outro dado [Valiente et al 2015]. Por exemplo, um arquivo de vídeo digital pode possuir um arquivo metadado que explica o seu conteúdo, por meio de informações como título, autor, assunto, tipo de mídia, etc.…”

Section: Repositórios De Reas E Metadadosunclassified

Análise de Repositórios de REAs em Relação ao Uso dos Padrões de Linked Open Data

Paula¹,

Deus²,

Barbosa³

2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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Recursos Educacionais Abertos (REAs) são uma parte muito importante para o conceito de Educação Aberta. Esses recursos são compartilhados de forma aberta, possibilitando seu uso, reuso e adaptação de forma livre. Um ponto importante para tornar eficiente a busca por REAs em repositórios pela Web é como o metadado desses recursos devem expor dados sobre o mesmo. Uma iniciativa global para definir padrões para propiciar acesso e interligação de dados abertos na Web é o Linked Open Data. Este artigo apresenta uma análise de repositórios de REAs a respeito do uso dos padrões de LOD em seus metadados. Ao final é feita uma discussão sobre os resultados dessa análise e as vantagens dessa adoção pelos repositórios.

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Section: Repositórios De Reas E Metadadosunclassified

Análise de Repositórios de REAs em Relação ao Uso dos Padrões de Linked Open Data

Paula¹,

Deus²,

Barbosa³

2020

Anais Do XXXI Simpósio Brasileiro De Informática Na Educação (SBIE 2020)

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“…There is another type of indexing by metadata that does not fit in the content of the LO but just indexes its structure, it is done most of the time manually by human beings, it is based on standards for example the LOM; confirming the usefulness of LO searching [21].…”

Section: Educational Search Enginesmentioning

confidence: 99%

A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

Guemmat

Ouahabi

2019

Int. J. Emerg. Technol. Learn.

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Educational search engines are important for users to find learning objects (LO). However, these engines have not reached maturity in terms of searching, they suffer from several worries like the deep extraction of notions which diminishes their performance. The purpose of this paper is to propose a new approach that allows depth extraction of LO’s notions to increase the relevance level of educational search engines. The proposed approach focuses on semi-automatic indexing of textual LO and more precisely the deeper relations of sentences that flesh out explanations. It based on linguistic structures and semantic distances between specific and generic notions according to OntOAlgO ontology. The notions obtained will be improved by learning object metadata (LOM) and will be represented semantically in final index. The tests performed on algorithmic LO, proving the usefulness of our approach to educational search engines. It increases the degree of precision and recall of notions extracted from LO.

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“…The proposed approach uses an adaptive tf-idf scoring and adaptive visual representation to achieve the selection of relevant digital resources. Valiente et al [21] reported an exploratory study based on the availability and suitability of keywords and classifications in metadata-based educational resources to improve collaborative learning between teachers and students through the search and analysis of learning resources from a large sample obtained from the Global Learning Objects Brokered Exchange (GLOBE). Segura et al [22] proposed an expansion of queries based on formal domain ontology in case of the search for learning resources in repositories.…”

Section: Introductionmentioning

confidence: 99%

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

Xiao¹,

Yang²,

Zhang³

2023

Int. J. Emerg. Technol. Learn.

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Ever since the human society has entered the era of big data, the quantity and type of digital learning resources on the Internet are increasing exponentially, and the requirement of students for learning resource retrieval is on the rise. However, the existing methods of learning resource retrieval generally overlook the overall knowledge systems that the students have already possessed, so it’s impossible for them to predict the students’ learning path or perform deviation adjustment. In view of these issues, this paper aims to study a new learning resource retrieval method based on multi-knowledge association mining. At first, the paper introduces the application of the Knowledge Graph Embedding (KGE) technology in learning resource retrieval, proposes the problem of learning resource retrieval, and points out the goal of learning resource retrieval. Then, a breadth-first soft-matching search algorithm is introduced to attain the multiple association paths between students and learning resources, and a retrieval module is constructed based on association path to further learn the association paths between students and learning resources within the framework of feature learning, and to predict the probability of interactions between students and learning resources. At last, this paper evaluates the association paths and uses experimental results to verify the validity of the proposed learning resource retrieval method.

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Adopting the metadata approach to improve the search and analysis of educational resources for online learning

Cited by 10 publications

References 7 publications

Análise de Repositórios de REAs em Relação ao Uso dos Padrões de Linked Open Data

Análise de Repositórios de REAs em Relação ao Uso dos Padrões de Linked Open Data

A Semantic Distances-Based Approach for a Deeply Indexing of Learning Objects

A New Learning Resource Retrieval Method Based on Multi-knowledge Association Mining

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