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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

International Journal of Computer Applications
© 2013 by IJCA Journal
Volume 66 - Number 4
Year of Publication: 2013
Hasnat Ahmad Hussny
Ahmed Mateen
Tasleem Mustafa
Muhammad Murtaza Nayyer

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

	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}


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.


  • A. Gunasekaran, Ronald D. McNeil and Dennis Shaul, "E-Learning: research and applications", Industrial and Commercial Training, 2002, 34(2), pp. 44-53.
  • Bechhofer, S. F. V. Harmelen, J. Hendler, I. Horrocks, D. Mcguinness, P. P. Schneijder and L. A. Stein. 2004. OWL Web Ontology Language Reference. Recommendation, World Wide Web Consortium (Available on-line with updates at http://www. w3. org/TR/owl-features/).
  • Cormode, G. and B. Krishnamurthy. "Key Differences between Web1. 0 and Web2. 0", February 13, 2008.
  • Cohen, E. B. and M. Nycz. 2006. Learning objects and e-learning: an informing science perspective. Interdisciplinary Journal of Knowledge and Learning Objects, 2(1) : 23-34.
  • Chinnici, R. , J. J. Moreau, A. Ryman and S. Weerawarana. 2007. Web services description language (WSDL) version 2. 0 part 1: Core language. World Wide Web Consortium, Recommendation (Available on-line with updates at http://www. w3. org/TR/wsdl20-primer).
  • C. G. and V. C. 2. "The use of Web 2. 0 Technologies and Services to support E-Learning Ecosystem to develop more effective Learning Environments", the Ninth IEEE International Conference on Advanced Learning Technologies", Australia/2008.
  • Cisos, K. J. , W. Pedrycz, R. W. Swiniarski and L. A. Kurgan. Data Mining A Knowledge Discovery Approach, Springer Publication, 2007. Available at: http://www. springer. com/978-0-387-33333-5.
  • Duane Merrill, "Mashups: The New Breed of Web Application", IBM Developer Works, 2006, retrieved from http://www. ibm. com/developerworks/xml/library/x-mashups. html, last access May 6, 2009.
  • Emory M. Craig, "Changing paradigms: managed learning environments and Web 2. 0". Campus-Wide Information Systems, 2007, 24 (3), pp. 152-161.
  • Fiaidhi, J. and S. Mohammed. "Learning Agents Framework Utilizing Ambient Awareness and Enterprise Mashup", "the International Journal of Instructional Technology and Distance Learning", 2008, 6(3).
  • Gerhard Wurzinger, V. Chang, et al. "Towards greater Flexibility in the Learning Ecosystem Promises and Obstacles of Service Composition for Learning Environments", Australia.
  • http://www. dontwasteyourtime. co. uk, Benefits of Collaborative Learning. Jul 8 . By David Hopkins.
  • http://en. wikipedia. org/wiki/Web_2. 0.
  • Han, J. , M. Kamber and J. Pei. 2011 Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco, C. A. , USA.
  • http://mivanova. blogspot. ca/2007/11/elearning-10-ecosystem-and-elearning-20. html.
  • http://en. wikipedia. org/wiki/Rich_Internet_application.
  • http://en. wikipedia. org/wiki/Folksonomy.
  • http://en. wikipedia. org/wiki/XML.
  • http://en. wikipedia. org/wiki/Mashup_(web_application_hybrid).
  • http://en. wikipedia. org/wiki/Web_Services_Description_Language.
  • http://en. wikipedia. org/wiki/Web_Service_Choreography
  • http://en. wikipedia. org/wiki/Attention_Profiling_Mark-up_Language.
  • http://en. wikipedia. org/wiki/Predictive_modelling.
  • http://www. gartner. com/it-glossary/predictive-modeling.
  • http://searchdatamanagement. techtarget. com/definition/predictive-modeling.
  • http://en. wikipedia. org/wiki/JSON
  • Hua, P. , C. P. H. Li, K. K. Chen and M. Wua. 2009. Accessing e-Learners' Knowledge for Personalization in e-Learning Environment. Journal of Research and Practice in Information Technology, 41(4) : 295-318.
  • Kuncheva, L. I. 2004. Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey.
  • Kavantzas, N. , D. Burdett, G. Ritzinger, T. Fletcher, Y. Lafon and C. Barreto. 2005. Web services choreography description language version 1. 0. World Wide Web Consortium, Candidate Recommendation (Available on-line with updates at http://www. w3. org/TR/ws-cdl-10/).
  • Klyne, G. , J. J. Carroll, and B. McBride. 2008. Resource description framework (RDF): Concepts and abstract syntax. W3C Recommendation. (Available on-line with updates at http://www. w3. org/TR/rdf-concepts).
  • Loma Uden and Ernesto Damiani, "The future of E-Learning: E-Learning ecosystem", Proceedings of the first IEEE International Conference on Digital Ecosystems and Technologies, Cairns, Australia, 2007, pp. 113-117.
  • Lee, I. and C. T. Bruin. 2012. Geographic Knowledge Discovery from Web 2. 0 Technologies for Advance Collective Intelligence. Slovenian Society Informatika 35, PP : 453-461.
  • L. I. Kuncheva. Combining Pattern Classifiers: Meth-ods and Algorithms. Wiley, New Jersey, 2004.
  • Miller, H. J. and J. Han. 2001. Geographic Data Mining and Knowledge Discovery: An Overview. Cambridge University Press, UK.
  • Manglo, F. and E. Miller. 2004. Rdf primer (Available on-line at http://www. w3. org/TR/rdf-primer).
  • Panda, S. S. , D. Chakraborty and S. K. Pal. 2007. Flank wear prediction in drilling using back propagation neural network and radial basis function network. Applied Soft Computing, 8(2): 858-871.
  • Price, C, "E-Learning Ecosystems: The Future of Learning Technology", retrieved from http://www. cyberdent. ca/index. php?option=com_content&task=view&id=20&Itemid=9, last access May 6, 2009.
  • Raspl, S. 2004. Workshop on Data Mining Standards, Services and Platforms the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (Available on-line at http://www. lac. uic. edu/workshops/dm-ssp04. htm).
  • S. Alag. Collective Intelligence in Action. Manning Publications, 2008.
  • Sclater, N. "Web 2. 0, Personal Learning Environments, and the Future of Learning Management Systems", 2008. http://www. igiglobal. com/Bookstore/Article. aspx?TitleId=42095.
  • Selwyn, N. "Web 2. 0 applications as alternative environments for informal learning a critical review", paper for OECD-KERIS expert meeting, London, UK.
  • Tsai, C. F. and J. W. Wu. 2008. Using neural network ensembles for bankruptcy prediction and credit scoring. Expert Systems with Applications, 34(4): 2639-2649.
  • Tim L. Wentling, Consuelo Waight, James Gallaher, Jason La Fleur, Christine Wang and Alaina Kanfer, "E-Learning A review of literature", retrieved from http://learning. ncsa. uiuc. edu/papers/elearnlit. pdf, last access May 6, 2009.
  • Uden, L. , I. T. Wangsa, et al. "The future of E-learning: E-learning ecosystem", "the Inaugural IEEE International Conference on Digital Ecosystems and Technologies (IEEE DEST 2007)".
  • Vanessa Chang and Christian Guetl, "E-Learning Ecosystem (ELES) A Holistic Approach for the Development of more Effective Learning Environment for Small-and-Medium Sized Enterprises (SMEs)", Proceedings of the first IEEE International Conference on Digital Ecosystems and Technologies, Cairns, Australia, 2007, pp. 420-425.
  • WU, D. , Z. Yang and L. Liang. 2006 . Using DEA-neural network approach to evaluate branch efficiency of a large Canadian bank. Expert Systems with Applications, 31: 108-11.