International Journal of Modern Education and Computer Science (IJMECS)
IJMECS Vol. 12, No. 4, 8 Aug. 2020
Cover page and Table of Contents: PDF (size: 911KB)
Proposal of a Digital Ecosystem Based on Big Data and Artificial Intelligence to Support Educational and Vocational Guidance
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Author(s)
Index Terms
Digital Ecosystem, guidance, Artificial Intelligence, Big data, SMA, Expert System, Ontology
Abstract
The work in this article focuses on the modelling of an intelligent digital ecosystem for educational and career guidance for students and young people seeking their first job or retraining. To do so, the multi-expert system paradigm was used to aggregate the different expertises required for a good guidance, the multi-agent system principle was used to have a modular and easily scalable ecosystem. Indeed, the agents of the system communicate with each other using the FIPA-ACL language, in a collaborative vision, throughout the orientation assistance process to perform tasks such as proposing business sectors, occupations, training, and training paths. The ontologies of the Semantic Web have been used to have a complete semantic description of the shared information and to promote communication between the different software agents of the ecosystem. Big Data principles have also been deployed to manage and exploit structured and unstructured data from different data sources related to the guidance ecosystem.
The ecosystem modeled in this way has several innovative and powerful technological and scientific aspects. Thus, in terms of design and modelling, the proposed ecosystem considers all the actors and factors involved in the guidance process, including labor market trends. In technological/scientific terms, it is based on methods that allow it to be modular and scalable.
Cite This Paper
Essaid EL HAJI, Abdellah Azmani, " Proposal of a Digital Ecosystem Based on Big Data and Artificial Intelligence to Support Educational and Vocational Guidance", International Journal of Modern Education and Computer Science(IJMECS), Vol.12, No.4, pp. 1-11, 2020.DOI: 10.5815/ijmecs.2020.04.01
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