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Reseach Article

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
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.

References
  1. A. Gunasekaran, Ronald D. McNeil and Dennis Shaul, "E-Learning: research and applications", Industrial and Commercial Training, 2002, 34(2), pp. 44-53.
  2. 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/).
  3. Cormode, G. and B. Krishnamurthy. "Key Differences between Web1. 0 and Web2. 0", February 13, 2008.
  4. 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.
  5. 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).
  6. 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.
  7. 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.
  8. 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.
  9. Emory M. Craig, "Changing paradigms: managed learning environments and Web 2. 0". Campus-Wide Information Systems, 2007, 24 (3), pp. 152-161.
  10. 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).
  11. Gerhard Wurzinger, V. Chang, et al. "Towards greater Flexibility in the Learning Ecosystem Promises and Obstacles of Service Composition for Learning Environments", Australia.
  12. http://www. dontwasteyourtime. co. uk, Benefits of Collaborative Learning. Jul 8 . By David Hopkins.
  13. http://en. wikipedia. org/wiki/Web_2. 0.
  14. Han, J. , M. Kamber and J. Pei. 2011 Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, San Francisco, C. A. , USA.
  15. http://mivanova. blogspot. ca/2007/11/elearning-10-ecosystem-and-elearning-20. html.
  16. http://en. wikipedia. org/wiki/Rich_Internet_application.
  17. http://en. wikipedia. org/wiki/Folksonomy.
  18. http://en. wikipedia. org/wiki/XML.
  19. http://en. wikipedia. org/wiki/Mashup_(web_application_hybrid).
  20. http://en. wikipedia. org/wiki/Web_Services_Description_Language.
  21. http://en. wikipedia. org/wiki/Web_Service_Choreography
  22. http://en. wikipedia. org/wiki/Attention_Profiling_Mark-up_Language.
  23. http://en. wikipedia. org/wiki/Predictive_modelling.
  24. http://www. gartner. com/it-glossary/predictive-modeling.
  25. http://searchdatamanagement. techtarget. com/definition/predictive-modeling.
  26. http://en. wikipedia. org/wiki/JSON
  27. 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.
  28. Kuncheva, L. I. 2004. Combining Pattern Classifiers: Methods and Algorithms. Wiley, New Jersey.
  29. 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/).
  30. 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).
  31. 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.
  32. 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.
  33. L. I. Kuncheva. Combining Pattern Classifiers: Meth-ods and Algorithms. Wiley, New Jersey, 2004.
  34. Miller, H. J. and J. Han. 2001. Geographic Data Mining and Knowledge Discovery: An Overview. Cambridge University Press, UK.
  35. Manglo, F. and E. Miller. 2004. Rdf primer (Available on-line at http://www. w3. org/TR/rdf-primer).
  36. 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.
  37. 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.
  38. 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).
  39. S. Alag. Collective Intelligence in Action. Manning Publications, 2008.
  40. 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.
  41. Selwyn, N. "Web 2. 0 applications as alternative environments for informal learning a critical review", paper for OECD-KERIS expert meeting, London, UK.
  42. 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.
  43. 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.
  44. 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)".
  45. 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.
  46. 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.
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