Interlinking educational resources to Web of Data through IEEE LOM

Rajabi, Enayat; Sicilia, Miguel–Ángel; Sánchez‐Alonso, Salvador

doi:10.2298/csis140330088r

Cited by 16 publications

(8 citation statements)

References 13 publications

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“…LGs that meet their needs quickly, these resources need to be categorized with the relevant type of information [12]. Early research on LG inventories propose the use of the LOM model, widely used for describing learning resources [13,14] and one of the key foundations to Learning Management Systems. LOM has 68 fields, organized in nine categories, that describe all aspects of learning resources [15,16]: ─ General -Main characteristics of the learning resource (title, description, etc.).…”

Section: Analysis Of Metadata Models For Learning Games 21 Charactermentioning

confidence: 99%

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Morie

Marfisi-Schottman

Goore

2020

Communications in Computer and Information Science

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Learning Games (LGs) have proven to be effective in a large variety of academic fields and for all levels; from kindergarten to professional training. They are therefore very valuable learning resources that should be shared and reused. However, the lack of catalogues that allow teachers to find existing LGs is a significant obstacle to their use in class. It is difficult for catalogues, or any type of search engine, to index LGs because they are poorly referenced. Yet, many researches have proposed elaborate metadata models for LGs. However, all these models are extensions of LOM, a metadata model that is widely used for referencing learning resources, but that contains more than 60 fields, of which more than half are irrelevant to LGs. The gap between these models and the information that game designers are willing to provide is huge. In this paper, we analyze the LG metadata models proposed in previous research to detect the fields that are specific to LGs and the fields that are irrelevant to LGs. We then propose LGMD (Learning Games Metadata Definition), an optimal lightweight metadata model that only contains the important information for LG indexing. LGMD reduces by two thirds the number of fields compared to the previous models. We confronted this model with the information actually provided by LG editors, by analyzing 736 LG page descriptions found online. This study shows that LGMD covers all the information provided by the LG editors.

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Section: Analysis Of Metadata Models For Learning Games 21 Charactermentioning

confidence: 99%

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Morie

Marfisi-Schottman

Goore

2020

Communications in Computer and Information Science

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“…In order to create a LG catalogue that covers all types of levels and educational fields and that is automatically updated with new LGs, it is necessary to reduce human intervention and switch to an automatic method that will scan the pages of the LG editors' webpages to retrieve the necessary information, analyze it and format it according to an indexing standard. This is where the fist difficulty appears: LG editors do not follow standards such as LOM (Learning Object Metadata) [21][22][23], MLR (Metadata for Learning Resources) [24] or ontologybased systems [25,26] to define their games [27]. This greatly complicates the automatic indexing task, since the system cannot immediately understand the information.…”

Section: Related Workmentioning

confidence: 99%

Information Extraction Model to Improve Learning Game Metadata Indexing

Morie¹,

Marfisi-Schottman²,

Tra³

2020

ISI

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The use of Learning Games (LGs) in schools is a success factor for students. The benefits they bring to the learning process should be widely disseminated at all levels of education. Currently, there are thousands of LGs that cover a large variety of educations fields. Despite this large choice of LGs, very few are used by teachers, due to the difficulty of finding and selecting suitable LGs. The aim of this paper is to propose an extraction model that will automatically collect the information about LGs directly from their web pages, in order to index them in a catalogue. The proposed ADEM (Automatic Description Extraction Model), browses the web pages describing LGs and does a first cleaning to remove any unnecessary information. Then a detection of description blocks, based on a certain number of criteria, identifies the regions containing the LG description text. Finally, an indexing on specific fields is performed. ADEM made it possible to automatically process 785 web pages to extract LG metadata indexing information. The results of this extraction process were validated by 20 teachers. This model therefore offers a promising starting point for better LG indexing and the creation of a complete catalogue.

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“…4 LOD Cloud -site que possui informações sobre o atual estado do LOD em relação a bases interligadas -https://lod-cloud.net/ -acessado em 28/09/2020. IX Congresso Brasileiro de Informática na Educação (CBIE 2020) Anais do XXXI Simpósio Brasileiro de Informática na Educação (SBIE 2020) [Rajabi et al 2014] apresentaram como expor metadados seguindo o padrão IEEE LOM 5 . Para isso, foi feita uma relação entre esse metadado e os padrões do LOD.…”

Section: Trabalhos Relacionadosunclassified

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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Interlinking educational resources to Web of Data through IEEE LOM

Cited by 16 publications

References 13 publications

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

LGMD: Optimal Lightweight Metadata Model for Indexing Learning Games

Information Extraction Model to Improve Learning Game Metadata Indexing

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

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