Untitled Document
   
You are from : ( )  
     
Untitled Document
Untitled Document
 

International Journal of Information Technology & Computer Science ( IJITCS )

Abstract :

To deliver effective personalization of Web-based Learning System (WBLS) users, it is important to provide matching instructional strategies to enhance users’ learning performance. Therefore it is necessary to identify different types of learning model that conducted by users in their learning progresses. This study utilizes kmeans clustering technique to understanding users behavior by using eight interaction variables. According to the results from k-means clustering, there are three different learning models in WBLS users and users in different level of knowledge and different type of cognitive style using different model to learn.

Keywords :

: Web-based learning system, usage model, human factors

References :

  1. M.Y. Ivory, R. Megraw, ”Evolution of web site design patterns,” ACM Trans. Inform. Syst. vol. 23, pp.463–497, 2005.
  2. J. Callan, A. Smeaton, M. Beaulieu, P. Borlund, P. Brusilovsky, M. Chalmers, , C. Lynch, J. Riedl, B. Smyth, U. Straccia, E. Toms, “Personalization and recommender systems in Digital Libraries,” Joint NSFEU DELOSWorking Group Report, URL http://www.ercim.org/publication/ws-proceedings/Delos- NSF/Personalisation.pdf. (2003)
  3. I. Zukerman, D.W. Albrecht, A. E. Nicholson, “Predicting users request on theWWW”. In: Proceedings of the 7th International Conference on User Modeling, UM99, Banff, Canada, pp. 275–284 ,1999.
  4. L. Harasim, “Shift happens: Online education as a new paradigm in learning,” The Internet and Higher Education, vol. 2, pp. 41−61, 2000.
  5. R. Riding, S. Rayner, Cognitive Styles and Learning Strategies. London: David Fulton Publishers, 1998.
  6. Y.H. Cho, J.K. Kim, S.H. Kim, “Apersonalized recommender system based onweb usagemining and decision tree,” Expert Syst. Appl. vol 23. ,pp.329–342, 2002.
  7. S.Y. Chen, R.D.Macredie, ”Cognitive modelling of student learning in web-based instructional programmes,” Int. J. Human-Comput. Interact. vol. 17, pp. 375–402, 2004.
  8. N. Ford, D. Miller, “Gender differences in Internet perception and use,” In: Electronic Library and Visual Information Research, Proceedings of the Third ELVIRA Conference, pp. 87–202, 1996.
  9. T. Mitchell, S. Chen, R. Macredie, “Hypermedia learning and prior knowledge: domain expertise vs. system expertise,” J. Comput. Assist. Learn. vol. 21, pp. 53–64, 2005.
  10. M.Y. Yi, Y.Hwang, “Predicting the use of web-based information systems, self-efficacy, enjoyment, learning goal orientation, and the technology acceptance model,” Int. J. Human-Comput. Stud. vol 59, pp. 431–449, 2003.
  11. G. Torkzadeh,T. P. VanDyke, “Effects of training on Internet self-efficacy and computer user attitudes,” Comput. In Human Behav. vol. 18, pp. 479–494, 2002.
  12. N. Ford, D. Miller, N. Moss, “Web search strategies and human individual differences. Cognitive and demographic factors, internet attitudes and approaches,” J. Am. Soc. Inform. Sci. Technol. vol. 56, pp. 741– 756, 2005.
  13. R.A. Palmquist, K. Kim, “Cognitive style and on-line database search experience as predictors of Web search performance,” J. Am. Soc. Inform. Sci. vol. 51, pp. 558–566, 2000.
  14. R. Riding, M. Grimley, “Cognitive style and learning from multimedia materials in 11-year children,” British Jou. of Edu. Tech., vol. 30, pp. 43-59, 1999.

Untitled Document
     
Untitled Document
   
  Copyright © 2013 IJITCS.  All rights reserved. IISRC® is a registered trademark of IJITCS Properties.