Keeping interoperability between IMS-LD scenarios in Educational Cloud Computing based on Semantic Indexing

Autor: Sara Ouahabi, Kamal El Guemmat
Rok vydání: 2019
Předmět:
Zdroj: 2019 1st International Conference on Smart Systems and Data Science (ICSSD).
DOI: 10.1109/icssd47982.2019.9002705
Popis: The purpose of this paper is to propose an effective system to automate the work between learning actors and manage interoperability between contents in the Educational Cloud (EC). Building e-learning systems in the cloud computing is a multidisciplinary endeavor that involves Learning Object (LO), Instructional Management Systems Learning Design (IMS-LD) specification, semi-automatic semantic indexing techniques according to ontologies, algorithms for the automatic processing of natural language (NLP) and system development framework. Our project implements a group of engaging, affectionate, and effective IMS-LD package equipped with abilities to facilitate and support reuse, sharing and identification of the LO between learning actors in the EC. We proposed a pedagogical system in EC that offer more benefits for the various actors to collaborate and to share LO flexibly.
Databáze: OpenAIRE