Pedagogical Indexed Arabic Text in Cloud E-Learning System

Nafaa Haffar, Mohsen Maraoui, Shadi Aljawarneh, Mohammed Bouhorma, Abdallah Altahan Alnuaimi and Bilal Hawashin
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Nafaa Haffar: University of Monastir, Monastir, Tunisia
Mohsen Maraoui: LaTice Lab, Faculty of Sciences of Monastir, University of Monastir, Monastir, Tunisia
Shadi Aljawarneh: Jordan University of Science and Technology, Faculty of Computer and Information Technology, Irbid, Jordan
Mohammed Bouhorma: Abdelmalek Essaâdi University, Tétouan, Morocco
Abdallah Altahan Alnuaimi: Al-Isra University, Amman, Jordan
Bilal Hawashin: Al-Zaytoonah University, Amman, Jordan

International Journal of Cloud Applications and Computing (IJCAC), 2017, vol. 7, issue 1, 32-46

Abstract: The Cloud E-Learning Systems for the Arabic language are relevant environments in many areas of training (teaching Arabic language) but also pose problems related to their creation tedious, costly in resources and time, and problems related to the search for information because of the increasing amount of information available and because of the methods of indexing, which is based on static methods such as keyword search that makes irrelevant the research process. For this, a new method of indexation is required. In this paper, a new Arabic text is proposed indexing approach using the creation of a new application profile of the LOM metadata schema (Learning Object Metadata) for the Arabic language. This profile includes the fields of LOM standard, and adds new fields for specific search information to Arabic language, and meets the needs of a teacher. Also, it's all using natural language processing tools like SAPA and AL-KHALIL.

Date: 2017
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