Querying Repositories of OER Descriptions: The Challenge of Educational Metadata Schemas Diversity

Hajri, Hiba; Bourda, Yolaine; Popineau, Fabrice

doi:10.1007/978-3-319-24258-3_64

Cited by 5 publications

(3 citation statements)

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“…The dilemma of the diversity and competed schemas is a topic that [54] addressed. Starting with making comparisons between schemas and reaching to achieve mapping among them, the authors used three bases to make the comparison: (1) using owl: EquivalentClasses, (2) owl: equivalentProperty, and (3) using SPARQL query.…”

Section: To Enable Representing Oers and Creativementioning

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: To Enable Representing Oers and Creativementioning

confidence: 99%

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

et al. 2018

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“…At the end of the kth week of the MOOC, let a learner L who acquires LP L (lo) in lo ∈ LO k , the recommendation process is triggered when: The aim of this module is to select a set of candidate OERs dealing with the knowledge element for which the recommendation process has been triggered. In order to find these resources, the system performs a keyword search in metadata stored in external accessible repositories of OERs metadata (Hajri et al, 2015). The metadata used in this search is "the description of the resource" which introduces the subject and the global idea of the resource.…”

Section: Presearch Modulementioning

confidence: 99%

“…To select these resources, our Pre Search module requests external repositories providing SPARQL endpoints. In order to manage the diversity of metadata schemas employed by these repositories, we use classes and properties as defined in the learning Object ontology of mapping (LOOM) introduced in (Hajri et al, 2015).…”

Section: Presearch Modulementioning

confidence: 99%

A System to Recommend Open Educational Resources during an Online Course

Hajri

Bourda

Popineau

2018

Proceedings of the 10th International Conference on Computer Supported Education

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Recently, personalization in Technology Enhanced Learning (TEL) has been researched extensively. With the spreading of online learning environments (OLE) as MOOCs and LMSs, a large number of learners with different characteristics and backgrounds can follow online courses. To support personalization, recommender systems can be used to provide each learner with learning objects helping him to reach his learning objectives. These recommendations are more specific to compute than usual recommendations (like consumer products). Furthermore, if they are included in a course, they depend not only on the learner's profile but also on the content of the course, because they need to fit with the course format at any point. At the same time, there is a growing number of open educational resources (OER) available to usefully enrich the content of online courses. To facilitate their reuse some of these OERs are described with metadata schemas following Linked Open Data principles (LOD). In this paper, we introduce a MOOC-based OER recommender system (MORS) that can be plugged in an OLE to provide recommendations of OERs to learners based on their profiles, the course profile and a process for calculating recommendations based on OERs metadata. This paper presents our approach that has been implemented in a MOOC platform: Open edX. However the proposed approach could be implemented in any OLE by using the same process to calculate recommendations, as long as the learner and the course profiles can be extracted.

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