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Toward Adaptive and Reusable Learning Content Using XML Dynamic Labeling Schemes and Relational Databases

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Advanced Intelligent Systems for Sustainable Development (AI2SD’2018) (AI2SD 2018)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 915))

Abstract

A learning object is “any digital resource that can be reused to support learning.” A learning object should meet several characteristics: interoperability, reusability, self-contentedness, accessibility, durability and adaptability. In order to achieve the accessibility, reusability and interoperability and in the aim of allowing learners the freedom to choose the learning objects they wish to appear in their courses we propose an approach to build an adaptive and reusable learning content. The general idea of our paper is to automatically generate a course for each learner according to his individual preferences to ensure a better adaptation. For this aim we opted for the XML language to represent the course materiel. So as to avoid the weaknesses of XML databases and to benefit from the strengths of Relational databases, the XML document of the course materiel will be stored in Relational databases and in order to identify the relationships between nodes and accelerate the query processing, we use the XML labeling schemes.

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Author information

Authors and Affiliations

  1. Faculty of Sciences and Technologies, Hassan 1st University, Settat, Morocco

    Zakaria Bousalem & Ilias Cherti

  2. Faculty of Sciences, Tetuan, Morocco

    Inssaf El Guabassi

Authors
  1. Zakaria Bousalem
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  2. Inssaf El Guabassi
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  3. Ilias Cherti
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Corresponding author

Correspondence to Zakaria Bousalem .

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Editors and Affiliations

  1. Faculty of Sciences and Techniques, Abdelmalek Essaâdi University, Tangier, Morocco

    Mostafa Ezziyyani

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Bousalem, Z., El Guabassi, I., Cherti, I. (2019). Toward Adaptive and Reusable Learning Content Using XML Dynamic Labeling Schemes and Relational Databases. In: Ezziyyani, M. (eds) Advanced Intelligent Systems for Sustainable Development (AI2SD’2018). AI2SD 2018. Advances in Intelligent Systems and Computing, vol 915. Springer, Cham. https://doi.org/10.1007/978-3-030-11928-7_71

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