We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Effectiveness of Data Mining - based E-learning system (DMBELS)

by M. Prema, S. Prakasam
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 66 - Number 19
Year of Publication: 2013
Authors: M. Prema, S. Prakasam
10.5120/11195-6386

M. Prema, S. Prakasam . Effectiveness of Data Mining - based E-learning system (DMBELS). International Journal of Computer Applications. 66, 19 ( March 2013), 31-36. DOI=10.5120/11195-6386

@article{ 10.5120/11195-6386,
author = { M. Prema, S. Prakasam },
title = { Effectiveness of Data Mining - based E-learning system (DMBELS) },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 66 },
number = { 19 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 31-36 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume66/number19/11195-6386/ },
doi = { 10.5120/11195-6386 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:23:27.708713+05:30
%A M. Prema
%A S. Prakasam
%T Effectiveness of Data Mining - based E-learning system (DMBELS)
%J International Journal of Computer Applications
%@ 0975-8887
%V 66
%N 19
%P 31-36
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

E-learning is emerging as the new paradigm of modern education. Most of the e-learning systems have limitations such as scarcity of content, lack of intelligent search and context sensitive personalization problems, which are the challenging tasks for researchers. This motivated the author to take up this problem and the method implemented through this work suggests the instructors to use the combination of the data mining based e-learning system (DMBELS) was designed. The main aim of the model developed is to get consistency in content delivery, quality content in learning materials, students self-learning concept, and performance improvement in their examination. A study has been conducted to measure the effectiveness of data mining technique based e-learning system (DMBELS) among the students of SCSVMV University in concepts of First Aid awareness course.

References
  1. Ajzen, I. , & Fishbein, M. (1977). Attitude–behavior relations: a theoretical analysis and review of empirical research. Psychological Bulletin, 84, 888–918.
  2. Arbaugh, J. B. , & Duray, R. (2002). Technological and structural characteristics, student learning and satisfaction with web-based courses – An exploratory study of two on-line MBA programs. Management Learning, 33(3), 331–347.
  3. Aronen, R. , & Dieressen, G. (2001). Improvement equipment reliability through e-Learning. Hydrocarbon Processing, 47–57.
  4. Bhattacherjee, A. (2001). Understanding information systems continuance: an expectation confirmation model. MIS Quarterly, 25(3), 270–351.
  5. Katz, Y. J. (2000). The comparative suitability of three ICT distance learning methodologies for college level instruction. Educational Media International, 37(1), 25–30.
  6. Katz, Y. J. (2002). Attitudes affecting college students' preferences for distance learning. Journal of Computer Assisted Learning, 18,2–9
  7. Lewis, C. (2002). Driving factors for e-Learning: an organizational perspective. Perspectives, 6(2), 50–54. Lin, Cathy S. , Wu, S. , & Tsai, R. J. (2005). Integrating perceived playfulness into expectation-confirmation model for web portal context. Information & Management, 42, 683–693.
  8. Piccoli, G. , Ahmad, R. , & Ives, B. (2001). Web-based virtual learning environments: a research framework and a preliminary assessment of effectiveness in basic IT skill training. MIS Quarterly, 25(4), 401–426.
  9. Wu, J. P. , Tsai, R. J. , Chen, C. C. , & Wu, Y. C. (2006). An integrative model to predict the continuance use of electronic learning systems: hints for teaching. International Journal on E-Learning, 5(2), 287–302.
  10. Khairil Imran Ghauth and, Nor Aniza Abdullah, (2009) An Empirical Evaluation Of Learner Performance In E-Learning Recommender Systems And An Adaptive Hypermedia System, pp 141-152.
  11. Jonassen, D. H. , Computers in the Classroom, Englewood Cliffs, NJ:Merrill, Keefe, J. W. (1987), in "Learning Style".
  12. Peters, J. , Jarvis, P. et al. , Adult Education, San Francisco, CA, Ed Rogers, A. , Teaching Adults, Buckingham: Open University Press
  13. Jemni, M. , & Nasraoui, O. (2009). Automatic recommendations for e-learning personalization based on web usage mining techniques and information retrieval. Educational Technology & Society, 12(4), 30–42.
  14. Liang, G. , Weining, K. & Junzhou, L. (2006). Courseware recommendation in e-learning system. Advances in Web Based Learning – ICWL2006, Springer Berlin/Heidelberg, 10-24.
  15. Kerkiri, T. , Manitsaris, A. & Mavridou, A. (2007). Reputation metadata for ecommending personalized e-learning resources. Proceedings of the Second International Workshop on Semantic Media Adaptation and Personalization, Uxbridge, 110-115.
Index Terms

Computer Science
Information Sciences

Keywords

Effectiveness Data

Powered by PhDFocusTM