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

Autor: Zakaria Bousalem, Ilias Cherti, Inssaf El Guabassi
Rok vydání: 2019
Předmět:
Zdroj: Advances in Intelligent Systems and Computing ISBN: 9783030119270
DOI: 10.1007/978-3-030-11928-7_71
Popis: 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.
Databáze: OpenAIRE