Promotion of self-learning by means of Open Educational Resources and semantic technologies

Chicaiza, Janneth; Piedra, Nelson; López, Jorge; Tovar, Edmundo

doi:10.1109/ithet.2015.7218024

Cited by 5 publications

(1 citation statement)

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“…Enabling self-learners to build their learning objectives, learning paths, skills and therefore their future jobs as accurate as possible is one of the core directions in personalization. In [77,78], the authors researched using LD to map current learners' experience, interests and objectives from one side and the open learning materials from the other side. The idea of making 'User Interests' as concepts of a knowledge domain is adopted.…”

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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Section: Personalization Learning Analysis and Assessments With Eldmentioning

confidence: 99%