Sharing Linkable Learning Objects with the use of Metadata and a Taxonomy Assistant for Categorization
| Autor: | Damiano Perri, Valentina Franzoni, Simonetta Pallottelli, Sergio Tasso |
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| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
Computer Science - Artificial Intelligence Computer science Other Computer Science (cs.OH) Learning object Context (language use) 02 engineering and technology Reuse E-learning World Wide Web Computer Science - Other Computer Science Moodle 020204 information systems Taxonomy (general) 0202 electrical engineering electronic engineering information engineering CMS Linked data Object (computer science) LMS Metadata G-Lorep Artificial Intelligence (cs.AI) Categorization CMS E-learning G-Lorep Learning management system Learning object Linked data LMS 020201 artificial intelligence & image processing Learning management system |
| Zdroj: | Computational Science and Its Applications – ICCSA 2019 ISBN: 9783030242954 ICCSA (2) |
| DOI: | 10.48550/arxiv.2212.05947 |
| Popis: | In this work, a re-design of the Moodledata module functionalities is presented to share learning objects between e-learning content platforms, e.g., Moodle and G-Lorep, in a linkable object format. The e-learning courses content of the Drupal-based Content Management System G-Lorep for academic learning is exchanged designing an object incorporating metadata to support the reuse and the classification in its context. In such an Artificial Intelligence environment, the exchange of Linkable Learning Objects can be used for dialogue between Learning Systems to obtain information, especially with the use of semantic or structural similarity measures to enhance the existent Taxonomy Assistant for advanced automated classification. |
| Databáze: | OpenAIRE |
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