Pedagogical Indexed Arabic Text in Cloud E-Learning System

2017 ◽  
Vol 7 (1) ◽  
pp. 32-46 ◽  
Author(s):  
Nafaa Haffar ◽  
Mohsen Maraoui ◽  
Shadi Aljawarneh ◽  
Mohammed Bouhorma ◽  
Abdallah Altahan Alnuaimi ◽  
...  
Keyword(s):  
Language Processing ◽  
Keyword Search ◽  
Research Process ◽  
Arabic Language ◽  
Learning System ◽  
Arabic Text ◽  
E Learning ◽  
Application Profile ◽  
Search For Information ◽  
Specific Search

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.

Automatic Organization and Generation of Presentation Slides for E-Learning

2012 ◽  
Vol 10 (3) ◽  
pp. 35-52 ◽  
Author(s):  
K. Sathiyamurthy ◽  
T. V. Geetha
Keyword(s):  
Distance Education ◽  
Language Processing ◽  
Learning System ◽  
Learning Material ◽  
Content Creation ◽  
Learning Content ◽  
E Learning ◽  
Processing Techniques ◽  
The Web ◽  
Extract Information

The effectiveness of an e-learning system for distance education to a large extent depends on the relevancy and presentation of learning content to the learner. The ability to gather documents on a particular topic from the web and adapt the contents of the document to suit the learner is an important task from the content creation perspective of e-learning. For the developer of e-learning material the provision to automatically extract, organize, and present content material would improve its effectiveness. This paper proposes to extract information from documents using language processing techniques and organizing the content into appropriate presentation slides for learning purposes using domain ontology and learning oriented pedagogy ontology.

scholarly journals Using wiki to build an e-learning system in statistics in the Arabic language

2012 ◽  
Vol 28 (2) ◽  
pp. 481-491 ◽  
Author(s):  
Taleb Ahmad ◽  
Wolfgang Härdle ◽  
Sigbert Klinke ◽  
Shafiqah Alawadhi
Keyword(s):  
Arabic Language ◽  
Learning System ◽  
E Learning

Indexing Serious Games to Meet Adaptive E-Learning System Needs

2020 ◽  
pp. 284-303
Author(s):  
Yassine El Borji ◽  
Mohammed Khaldi
Keyword(s):  
Serious Games ◽  
Main Idea ◽  
Learning Objects ◽  
Learning Systems ◽  
Learning System ◽  
The Other ◽  
E Learning ◽  
Technical Sense ◽  
Application Profile ◽  
Educational Field

This chapter aims to strengthen the integration of serious games in the educational field by providing tools to monitor and assist the progress of learners/players. The main idea is to address the integration aspects and the deployment of serious games in adaptive e-learning systems based on the automatic package and the export of serious games as reusable learning objects (LO). This integration will allow SGs to benefit from the tracking and support features offered by the LMS. On the other hand, LMS can supplement their training offer and reach a certain maturity. The approach aims to meet the specific needs of SGs in terms of metadata so that they can be described, indexed, and capitalized. This is a new application profile of the IEEE LOM standard entitled “SGLOM” integrating fields to describe SGs not only in a technical sense but also by examining the pedagogical and playful criteria. The authors also focus on the integration and extraction aspects of SGs in an LMS using the ADL SCORM 2004 data model that defines how content can be packaged as a SCORM PIF (package interchange file).

scholarly journals Natural Language Processing for the Analysis of the Political Characterisation of Migration in the Croatian Political Discourse

2020 ◽  
Vol 22 (3) ◽  
pp. 517-532
Author(s):  
Gabriele De Luca ◽  
Marko Beck
Keyword(s):  
Machine Learning ◽  
Political Parties ◽  
Language Processing ◽  
Political Discourse ◽  
Research Process ◽  
Learning System ◽  
Liberal Party ◽  
War Of Independence ◽  
Political Analysis ◽  
Minority Party

This paper tackles the issue of analyst bias in performance of comparative political analyses on political discourse, by leveraging data and machine-learning over human prior knowledge. The case studied is characterization of the issue of migration in the Croatian political discourse, which was chosen arbitrarily. We developed a machine-learning system that identifies most prominent features in the Croatian political discourse, with regards to migration and were interested solo in comparative political analysis in political science. This system does not rely on human judgement on the part of the researchers, and can be thus considered to be “objective”, short of possible sampling or selection bias. It is replicable. If provided, the same dataset and algorithm used, same conclusions should be reached by any scientist. This result was achieved by creating a text corpus from news items and press releases extracted from the websites of Croatian political parties currently represented in the Parliament. Available and collected data consist of public announcements mainly from IDS (Istarski Demokratski Sabor / Istrian Democratic Assambly), SDSS (Samostalna Demokratska Srpska Stranka / Independed Democratic Serb Party) and HSLS (Hrvatska Socijalno Liberalna Stranka / Croatian Social Liberal Party). Data analyzed suggests three dominant phrases of the research process. All political parties had similar political stand towards pointed out issues. Three most significant phrases were determined. First phrase is related to words “Demography” and “Reduction” and finding suggest that most analyzed articles relates towards migration of Croatian citizens in connection to economic hardships of some kind. Phrase two is related to words “Border” and “Croatia-Serbia” which strongly indicates relation to migration and is related towards inter-Balkan migration, mostly connected with consequences of the Croatian War of Independence from 1990’s, and is of most interest to SDSS, a Serb minority party in Croatia. Phrase three is related towards Marrakesh Agreement (Global Compact for Safe, Orderly and Regular Migration), where most of analyzed data shows that parties have a constructive but ambivalent stance towards migration from the third countries. Research conducted on available data, shows that wide spread international migration is not in the focus of most Croatian political parties, while topics and interest for inter-Balkan and Croatian economic/political migration dominates Croatian political spectre

scholarly journals A Survey of Arabic Text Classification Models

2018 ◽  
Vol 8 (6) ◽  
pp. 4352 ◽  
Author(s):  
Ahed M. F. Al-Sbou
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Classification Models ◽  
Natural Languages ◽  
Text Organization ◽  
Arabic Text Classification ◽  
Arabic Language Processing

<p>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</p>

scholarly journals Towards relation extraction from Arabic text: a review

2019 ◽  
Vol 5 (5) ◽  
pp. 212-215
Author(s):  
Abeer AlArfaj
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
Relation Extraction ◽  
Arabic Language ◽  
Extraction Methods ◽  
The State ◽  
Semantic Relations ◽  
Arabic Text ◽  
Taxonomic Relation

Semantic relation extraction is an important component of ontologies that can support many applications e.g. text mining, question answering, and information extraction. However, extracting semantic relations between concepts is not trivial and one of the main challenges in Natural Language Processing (NLP) Field. The Arabic language has complex morphological, grammatical, and semantic aspects since it is a highly inflectional and derivational language, which makes task even more challenging. In this paper, we present a review of the state of the art for relation extraction from texts, addressing the progress and difficulties in this field. We discuss several aspects related to this task, considering the taxonomic and non-taxonomic relation extraction methods. Majority of relation extraction approaches implement a combination of statistical and linguistic techniques to extract semantic relations from text. We also give special attention to the state of the work on relation extraction from Arabic texts, which need further progress.

Integrated E-Learning System and Its Practice

2004 ◽  
pp. 97-131 ◽  
Author(s):  
Toshio Okamoto ◽  
Mizue Kayama
Keyword(s):  
Higher Education ◽  
Natural Language Processing ◽  
Language Processing ◽  
Management System ◽  
Learning System ◽  
Educational Method ◽  
Statistical Natural Language Processing ◽  
E Learning ◽  
Flexible Framework ◽  
Data Log

In this chapter, we present an intelligent media oriented e-learning system. In this system, we have developed a LMS (Learning Management System), some learning control systems and some learning media, with a flexible framework. It is intended to provide a collaborative workplace to encourage interactions among lecturer/learners. Moreover, we propose an innovative educational method of a cooperative link between a university and an industry for higher education. We analyze these results and the problems we encountered, as well as offer constructive solutions. Furthermore, we have developed some intelligent media such as an analyzer/summarizer by the statistical natural language processing for data log of discussion process to encourage/aware discussion/negotiation between learners and an automatic reporting processor.

Improving Arabic Text Classification Using P-Stemmer

2020 ◽  
Vol 13 ◽  
Author(s):  
Tarek Kanan ◽  
Bilal Hawashin ◽  
Shadi Alzubi ◽  
Eyad Almaita ◽  
Ahmad Alkhatib ◽  
...  
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Text Categorization ◽  
English Language ◽  
Arabic Language ◽  
Online News ◽  
Support Vector ◽  
Arabic Text ◽  
Fast Learning ◽  
Arabic Text Classification

Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.

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