Recommendation of open educational resources. An approach based on linked open data

Chicaiza, Janneth; Piedra, Nelson; Lopez-Vargas, Jorge; Tovar, Edmundo

doi:10.1109/educon.2017.7943018

Cited by 22 publications

(25 citation statements)

References 14 publications

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“…This is done in order to update learning objects' properties and provide diverse and adaptive recommendations. Some studies make use of ontologies and open source RDF data to leverage semantic content, and define recommendation algorithms suitable for linked data (Chicaiza et al, 2017;Ruiz-Iniesta et al, 2014;Sun et al, 2017). Moreover, (Chicaiza et al, 2015) tries to define an open linked vocabulary to describe user profiles, in order to facilitate recommendations.…”

Section: Semantic and Ontology Based Methodsmentioning

confidence: 99%

“…The area of OER recommendation systems has enormous development potential. The available literature on OER based content recommendations to learners is currently limited (Chicaiza et al, 2017) and there is no signal that factors related to typical lifelong learning goals (skills, jobs) play any role here. To structure recent developments, we clustered available studies into the following four categories:…”

Section: Oer Recommendationmentioning

confidence: 99%

“…These are done by collecting properties of users and OERs using various approaches such as building (or reusing existing) ontologies (Wan and Niu, 2018;Sun et al, 2017), conducting user behavior analysis in social networks (Lopez-Vargas et al, 2014), or applying Text Mining techniques to identify similar OER-Documents (Duffin et al, 2007). Nevertheless, due to the lack of personalized services like high quality search and recommendation, the popularity of OERs has been limited in most user groups (typically educators or lifelong learners) (Sun et al, 2018;Ruiz-Iniesta et al, 2014;Chicaiza et al, 2015;Ha et al, 2011;Chicaiza et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

Tavakoli

Mol

Kismihók

2020

Proceedings of the 12th International Conference on Computer Supported Education

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In this paper, we suggest a novel method to aid lifelong learners to access relevant OER based learning content to master skills demanded on the labour market. Our software prototype 1) applies Text Classification and Text Mining methods on vacancy announcements to decompose jobs into meaningful skills components, which lifelong learners should target; and 2) creates a hybrid OER Recommender System to suggest personalized learning content for learners to progress towards their skill targets. For the first evaluation of this prototype we focused on two job areas: Data Scientist, and Mechanical Engineer. We applied our skill extractor approach and provided OER recommendations for learners targeting these jobs. We conducted in-depth, semi-structured interviews with 12 subject matter experts to learn how our prototype performs in terms of its objectives, logic, and contribution to learning. More than 150 recommendations were generated, and 76.9% of these recommendations were treated as useful by the interviewees. Interviews revealed that a personalized OER recommender system, based on skills demanded by labour market, has the potential to improve the learning experience of lifelong learners.

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Section: Semantic and Ontology Based Methodsmentioning

confidence: 99%

Section: Oer Recommendationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

Tavakoli

Mol

Kismihók

2020

Proceedings of the 12th International Conference on Computer Supported Education

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“…Many of these works have focused on just one repository, repositories sharing a common taxonomy or repositories conforming to the principles of Linked Data. An example of the latter is [9], which presents a recommendation framework based on Linked Open Data to make to recommend new OER. Other related works focus mainly on a very specific task.…”

Section: Related Workmentioning

confidence: 99%

On the Identification of Several Key Issues on OER Discovery for Smart Learning Environments

Molins-Ruano

Jurado

Rodríguez

2019

13th International Conference on Ubiquitous Computing and Ambient ‪Intelligence UCAmI 2019‬

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The best Open Educational Resources (OER) for each final user can be hard to find. OER come from lots of sources, in many different formats, conforming to a diverse logical structure and each user may present different objectives depending on their role in the teaching/learning process and their context. Previous attempts have only focused on one kind of technologies. A new approach that embraces diversity, may gain from the potential synergies of sharing resources in the development of the final recommendation system and the exploitation of the data. In this work, we aim at identifying the main challenges facing the field of OER recommendation, for a potential architecture model.

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“…Based on the comparison that they did between their automatic approach and the manual ones, it showed that the extracted concepts can generate good summarization of document contents. On the other hand, the challenging of providing recommendations to anonymous users with a minimum amount of users' information in a heterogeneous OER environment that engages heterogeneous types of users is discussed in [80]. The need of going in this direction is because classical approaches based on enough historical information about users does not fit into such OER repositories.…”

Section: Personalization Learning Analysis and Assessments With Eldmentioning

confidence: 99%

Added Values of Linked Data in Education: A Survey and Roadmap

et al. 2018

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Education values such as knowledge sharing, and the linked data (LD) abilities such as interoperability are in perfect harmony. Much research has exploited that and provided important contributions and improvements in education through LD. International universities, large open education repositories, OpenCourseWare (OCW) and Massive Open Online Courses (MOOCs) initiatives, educational search engines, blending and adaptive learning, learning analysis and other various areas were the targets of many works on leveraging LD. However, this research exists in a scattered way without any type of categorization or organization. In this paper, we present a survey on the current works in educational linked data (ELD) to provide a starting point and a comprehensive roadmap to help researchers in recognizing the main tracks in ELD area. In addition, the paper extracted the common life cycle, outcome datasets and vocabularies from the overall presented works. The paper also provides samples of applications that exhibit the practical benefit of adopting LD in the various tracks and highlights the challenges that each track faced during the utilization of LD. Pioneer ELD’s projects, other existing overviews and landscapes and the most used tools based on the stages and prevalent are presented. Lastly, discussion and recommendations were provided based on the overall study.

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Recommendation of open educational resources. An approach based on linked open data

Cited by 22 publications

References 14 publications

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

Labour Market Information Driven, Personalized, OER Recommendation System for Lifelong Learners

On the Identification of Several Key Issues on OER Discovery for Smart Learning Environments

Added Values of Linked Data in Education: A Survey and Roadmap

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