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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

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International Journal of Computer Applications
© 2013 by IJCA Journal
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 and Muhammad Murtaza Nayyer. Article: E-Learners 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):1-9, March 2013. Full text available. BibTeX

@article{key:article,
	author = {Hasnat Ahmad Hussny and Ahmed Mateen and Tasleem Mustafa and Muhammad Murtaza Nayyer},
	title = {Article: E-Learners 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},
	year = {2013},
	volume = {66},
	number = {4},
	pages = {1-9},
	month = {March},
	note = {Full text available}
}

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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