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Reseach Article

Enabling Effective Personalized Learning: Determinants for Knowledge based Web Information Retrieval Systems

by Y.d. Jayaweera, Md. Gapar Md. Johar, S.n. Perera
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 116 - Number 2
Year of Publication: 2015
Authors: Y.d. Jayaweera, Md. Gapar Md. Johar, S.n. Perera
10.5120/20309-2352

Y.d. Jayaweera, Md. Gapar Md. Johar, S.n. Perera . Enabling Effective Personalized Learning: Determinants for Knowledge based Web Information Retrieval Systems. International Journal of Computer Applications. 116, 2 ( April 2015), 19-24. DOI=10.5120/20309-2352

@article{ 10.5120/20309-2352,
author = { Y.d. Jayaweera, Md. Gapar Md. Johar, S.n. Perera },
title = { Enabling Effective Personalized Learning: Determinants for Knowledge based Web Information Retrieval Systems },
journal = { International Journal of Computer Applications },
issue_date = { April 2015 },
volume = { 116 },
number = { 2 },
month = { April },
year = { 2015 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume116/number2/20309-2352/ },
doi = { 10.5120/20309-2352 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:55:58.975475+05:30
%A Y.d. Jayaweera
%A Md. Gapar Md. Johar
%A S.n. Perera
%T Enabling Effective Personalized Learning: Determinants for Knowledge based Web Information Retrieval Systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 116
%N 2
%P 19-24
%D 2015
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In the digitized World, information is entangled in a mesh of unstructured web. Finding and retrieving relevant web resources to suit the user's information requirement is a challenge. Moreover, understanding and adapting to cater to different user information requirements is also an uphill task. To achieve the desired outcome, it is needed to have user accepted technology. Therefore, web information retrieval systems, especially search engines, should be user centered. Technology Acceptance Model (TAM) provides a basis with which one traces how external variables influence belief, attitude, and intention to use. Two cognitive beliefs are posited by TAM; perceived usefulness and perceived ease of use. This empirical study explores the influence of Users and Environment characteristics on a modern web information retrieval system. This paper analyzes the variables to determine perceptions of usefulness, attitude and preferences leading towards frequent factors to influence typical TAM results.

References
  1. Simon H. A. (1997). The future of information systems. Annals of Operations Research, 71, 3-14.
  2. Edmunds A. and Morris A. (2000). The problem of information overload in business organisations: a review of the literature. Int. J. of Information Management, 17-28.
  3. Rose D. E. and Levinson D. (2004), Understanding user goals in web search, In Proceedings of WWW 2004, pp. 13-19, New York, NY, USA. ACM Press.
  4. Davis F. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly 13 (3) 319–340.
  5. Davis F. D. and Venkatesh V. (2004). Toward pre-prototype user acceptance testing of new information systems: implications for software project management, IEEE Transactions on Engineering Management 51 (1) 31–46.
  6. Järvelin K. and Kekäläinen J. (2002), Cumulated Gain-based Evaluation of IR Techniques.
  7. Page L. and Brin S. (1998). The anatomy of a large-scale hypertext web search engine, Proceeding of the seventh International World Wide Web Conference.
  8. Chakrabarti S. , van den Berg M. H. , and Dom B. E. (1999). Distributed Hypertext Resource Discovery Through Examples, Proceedings of the 25th VLDB Conference, Edinburgh, Scotland.
  9. Yang Q. , Wang H. F. , Wen J. R. , Zhang G. , Lu Y. , Lee K. F. and Zhang H. J. (2000). Toward a Next Generation Search engine, Proceedings of the Sixth Pacific Rim Artifact Artificial Intelligence Conference, Melborne, Australia.
  10. Al-Dallal, and Sami A. (2012). Enhancing recall and precision of web search using genetic algorithm, Brunel University, School of Information Systems, Computing and Mathematics.
  11. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.
  12. Taylor, Shirley and Todd, Peter. (1995). "Assessing IT Usage: The Role of Prior Experience," MIS Quarterly, (19: 4).
  13. Venkatesh, V. , Morris, M. G. , Davis, G. B. , and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly. 27(3); p. 425-478.
  14. Venkatesh, V. , Thong, J. Y. L. , and Xin, X. (2012). "Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology," MIS Quarterly (36:1), 157-178.
  15. Sekaran U. , and Bougie R. (2009). Research Methods for Business: A Skill Building Approach, 324-328.
  16. Kaiser, H. F. (1974). An index of factorial simplicity. Psychometrika, 39, 31-36.
  17. Viswanath V. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology, MIS Quarterly 36 (1) 157-178.
Index Terms

Computer Science
Information Sciences

Keywords

Information Retrieval Technology Acceptance Model (TAM) Learner Intention Environment Characteristics