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E-Learner’s Collective Intelligent System Framework: Web Mining for Personalization in E-Learning 2.0 Ecosystem using Web 2.0 Technologies

by Hasnat Ahmad Hussny, Ahmed Mateen, Tasleem Mustafa, Muhammad Murtaza Nayyer
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 4
Year of Publication: 2013
Authors: Hasnat Ahmad Hussny, Ahmed Mateen, Tasleem Mustafa, Muhammad Murtaza Nayyer
10.5120/11069-5987

Hasnat Ahmad Hussny, Ahmed Mateen, Tasleem Mustafa, Muhammad Murtaza Nayyer . E-Learner’s Collective Intelligent System Framework: Web Mining for Personalization in E-Learning 2.0 Ecosystem using Web 2.0 Technologies. International Journal of Computer Applications. 66, 4 ( March 2013), 1-9. DOI=10.5120/11069-5987

@article{ 10.5120/11069-5987,
author = { Hasnat Ahmad Hussny, Ahmed Mateen, Tasleem Mustafa, Muhammad Murtaza Nayyer },
title = { E-Learner’s Collective Intelligent System Framework: Web Mining for Personalization in E-Learning 2.0 Ecosystem using Web 2.0 Technologies },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 4 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 1-9 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number4/11069-5987/ },
doi = { 10.5120/11069-5987 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:21:25.030956+05:30
%A Hasnat Ahmad Hussny
%A Ahmed Mateen
%A Tasleem Mustafa
%A Muhammad Murtaza Nayyer
%T E-Learner’s Collective Intelligent System Framework: Web Mining for Personalization in E-Learning 2.0 Ecosystem using Web 2.0 Technologies
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 4
%P 1-9
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

E-Learning 2. 0 ecosystem has turn out to be a trend in the world nowadays. The term E-Learning 2. 0 ecosystem was coined that came out during the emergence of Web 2. 0 technologies. Most of the researches overlook a deep-seated issue in the e-learner's foregoing knowledge on which the valuable intelligent systems are based. This research utilizes the e-Learner's collective intelligence knowledge and extracts useful information for appropriate target courses or resources as a part of a personalization procedure to construct the e-Learner's collective intelligent system framework for recommendation in e-learning 2. 0 ecosystem. This research based on a novel web usage mining techniques and introduces a novel approach to collective intelligence with the use of mashup and web 2. 0 technology approach to build a framework for an E-Learning 2. 0 ecosystem. It is incorporated in predictive model efficiently based on back-propagation network (BPN). A prototype system, named E-learner's Collective Intelligence System Framework, has been proposed which has features such as self-regulation, reusability, lightweight, end user oriented, and openness. To evaluate the proposed approach, empirical research is conducted for the performance evaluation.

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Index Terms

Computer Science
Information Sciences

Keywords

E-Learning 2. 0 Ecosystem Web Mining Web 2. 0 Technologies Neural Network Collective Intelligence Mashup Personalization Recommendation

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