Metadata Analysis of Open Educational Resources

Tavakoli, Mohammadreza; Elias, Mirette; Kismihók, Gábor; Auer, Sören

doi:10.1145/3448139.3448208

Cited by 9 publications

(7 citation statements)

References 16 publications

(30 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…And OER could be relative to learning pathways in MOOCs. General OER Recommender systems [11], [12], [14], [15], [16], [17], [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33], [30], [30], [30], [30], [42] , [44], [45] , [46], [49], [50], [51], [52], [53], [54], [56], [57], [58], [59] [100], [104], [111], [113], [114], [126], [127], [129], [131] OER in MOOCs [28],…”

Section: Figmentioning

confidence: 99%

“…OER Recommenders for teachers or could be adopted for teaching purposes [34], [35], [36], [17], [37], [31], [20], [36], [22], [39], [24], [25], [26] [27], [40], [29], [41], [42], [43], [44], [45], [46], [47], [31], [48], [49], [50], [51], [52], [53], [54], [124], [125], [128], [130] Generalities Literature reviews, SLR, surveys,, [63], [64], [65], [66] , [67], [68], [69], [70], [71], [72], [73], [74], [75], [76], [77],…”

Section: Figmentioning

confidence: 99%

See 1 more Smart Citation

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

background: In recent years, recommender systems (RS) have grown in popularity among learning and education stakeholders, while open educational resources (OER) have begun to be deployed to improve the education field. The selection of OERs should not be haphazard; rather, it should be based on the learning objectives and (RS) commonly utilized to overcome the abundance of OER. Objective: The aim of this paper is to examine the current advances in OER recommenders and the different commonly used recommendation approaches and technical features, to position our future research alongside the existing. Method: A systematic literature review with a deductive approach using the grounded theory technique, combining quantitative and qualitative analysis by adopting open coding (inductive and deductive) for content analysis. Databases: we employed Google Scholar as a search engine to crawl data. Results: The findings are presented according to the (Bernard, H. Russell .2000) process. We present the results of the analysis using quotes from interviews that illuminate the theory (exemplars). Conclusion: The review reveals an emerging interest in the area; nevertheless, hybrid interactive systems; chatbot-based, dedicated to course creators are lacking. Further and future research is needed

show abstract

Section: Figmentioning

confidence: 99%

Section: Figmentioning

confidence: 99%

Systematic Literature Review on Open Educational Resources Recommender Systems

Rima

Meriem²,

Ajhoun³

2022

Int. J. Interact. Mob. Technol.

View full text Add to dashboard Cite

show abstract

“…Furthermore, there is a lack of high-quality services, such as search and recommendation services. Although attempts to automatize quality assessment, they do not rely on models that can predict the quality based on metadata or an intelligent model [19].…”

Section: Introductionmentioning

confidence: 99%

Bringing Underused Learning Objects to the Light: A Multi-agent Based Approach

Behr

Cascalho

Mendes

et al. 2022

Progress in Artificial Intelligence

View full text Add to dashboard Cite

The digital learning transformation brings the extension of the traditional libraries to online repositories. Learning object repositories are employed to deliver several functionalities related to the learning object's lifecycle. However, these educational resources usually are not described effectively, lacking, for example, educational metadata and learning goals. Then, metadata incompleteness limits the quality of the services, such as search and recommendation, resulting in educational objects that do not have a proper role in teaching/learning environments. This work proposes to bring an active role to all educational resources, acting on the analysis generated from the usage statistics. To achieve this goal, we created a multi-agent architecture that complements the common repository's functionalities to improve learning and teaching experiences. We intend to use this architecture on a repository focused on ocean literacy learning objects. This paper presents some steps toward this goal by enhancing, when needed, the repository to adapt itself.

show abstract

“…These ERs are available in many different formats, such as videos, slide decks, audio recordings from lectures, digital textbooks, or simple web pages. Furthermore, ERs and OERs usually come with low-quality metadata [33], and they are isolated from other, content-wise similar ERs. That is one of the crucial reasons for lacking high-quality services, such as recommendation and search services, based on OERs [32].…”

Section: Introductionmentioning

confidence: 99%

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Ilkou

Abu-Rasheed

Tavakoli

et al. 2021

The Semantic Web – ISWC 2021

Self Cite

View full text Add to dashboard Cite

With the increased dependence on online learning platforms and educational resource repositories, a unified representation of digital learning resources becomes essential to support a dynamic and multi-source learning experience. We introduce the EduCOR ontology, an educational, career-oriented ontology that provides a foundation for representing online learning resources for personalised learning systems. The ontology is designed to enable learning material repositories to offer learning path recommendations, which correspond to the user’s learning goals and preferences, academic and psychological parameters, and labour-market skills. We present the multiple patterns that compose the EduCOR ontology, highlighting its cross-domain applicability and integrability with other ontologies. A demonstration of the proposed ontology on the real-life learning platform eDoer is discussed as a use case. We evaluate the EduCOR ontology using both gold standard and task-based approaches. The comparison of EduCOR to three gold schemata, and its application in two use-cases, shows its coverage and adaptability to multiple OER repositories, which allows generating user-centric and labour-market oriented recommendations.Resource: https://tibonto.github.io/educor/.

show abstract

Metadata Analysis of Open Educational Resources

Cited by 9 publications

References 16 publications

Systematic Literature Review on Open Educational Resources Recommender Systems

Systematic Literature Review on Open Educational Resources Recommender Systems

Bringing Underused Learning Objects to the Light: A Multi-agent Based Approach

EduCOR: An Educational and Career-Oriented Recommendation Ontology

Contact Info

Product

Resources

About