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A Model for Assessing and Enhancing Efficiency of E-learning systems

by Mohamed Attia, Manal A. Abdel-Fattah, Sayed Abd El-Gaber
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 156 - Number 6
Year of Publication: 2016
Authors: Mohamed Attia, Manal A. Abdel-Fattah, Sayed Abd El-Gaber
10.5120/ijca2016912521

Mohamed Attia, Manal A. Abdel-Fattah, Sayed Abd El-Gaber . A Model for Assessing and Enhancing Efficiency of E-learning systems. International Journal of Computer Applications. 156, 6 ( Dec 2016), 31-42. DOI=10.5120/ijca2016912521

@article{ 10.5120/ijca2016912521,
author = { Mohamed Attia, Manal A. Abdel-Fattah, Sayed Abd El-Gaber },
title = { A Model for Assessing and Enhancing Efficiency of E-learning systems },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2016 },
volume = { 156 },
number = { 6 },
month = { Dec },
year = { 2016 },
issn = { 0975-8887 },
pages = { 31-42 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume156/number6/26715-2016912521/ },
doi = { 10.5120/ijca2016912521 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:01:53.398205+05:30
%A Mohamed Attia
%A Manal A. Abdel-Fattah
%A Sayed Abd El-Gaber
%T A Model for Assessing and Enhancing Efficiency of E-learning systems
%J International Journal of Computer Applications
%@ 0975-8887
%V 156
%N 6
%P 31-42
%D 2016
%I Foundation of Computer Science (FCS), NY, USA
Abstract

There are few studies on evaluation of Learning Management Systems (LMS) within higher educational organizations. Moreover, most of earlier studies have many shortages in its evaluation methods. For example, it focuses on the unilateral view of the evaluation method; it doesn’t show weights for each different criterion effecting LMS and doesn’t rank its importance. This research proposes an evaluation criteria model that avoids all shortcomings on earlier studies as follows: By evaluating the LMS on multidimensional way, discovering new relations between different criteria, determining weights for each criterion and ranking most critical criteria effecting on LMS. The evaluation criteria consist of six dimensions technology, system and service quality, communication, effectiveness of content, instructors and students dimensions. Each dimension encompasses of set of other criteria. A survey questionnaire based on these evaluation criteria and its relations has been applied to 100 learners of the British university of Egypt, 125 learners on Helwan University and set of instructors at both universities. Then, verifying questions by holding a meeting with set of experts. Descriptive statistics were run to analyze the collection of data, delete outlier of it and test data reliability. Collective of Artificial intelligence (AI) methods are used for ranking each criterion and Dempster-Shafer theory is used to compare between different results of AI for obtaining the most critical ten ranking criteria based on achieving satisfaction for both students and instructors. The results of research showing that communication dimension, responsiveness and availability of instructors on LMS and degree of interactive courses are the highest three critical criteria for British university. It also sheds light on the attitude of instructors and students towards technology in addition to the easy access of any services or content and responsiveness of instructors on Helwan University.

References
  1. Sevgi Ozkan (2009). Multidimensional students’ evaluation of e-learning systems in the higher education context: An empirical investigation. Computers & Education 53 (2009) 1285–1296.
  2. Mohamed attia& Manal A. Abdel-Fattah & Sayed Abd El-Gaber (2015). Constructing Multi-Dimensional Criteria Model for Evaluating E-learning Systems Efficiency in the Higher Educational. International Journal of Advanced Research in Computer Science Volume 5, Issue 1
  3. Shee, D. Y., & Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers Education, 50(3), 894–905 .
  4. Shee, D. Y., & Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers Education, 50(3), 894–905.
  5. Bishop, Christopher M. (2005) Neural Networks for Pattern Recognition. Oxford University Press
  6. Chen, Sheng, Xia Hong and Chris J. Harris, "Orthogonal Forward Selection for Constructing the Radial Basis Function Network with Tunable Nodes", 2005.
  7. Cristianini, Nello and John Shawe-Taylor: An Introduction to Support Vector Machines and other kernel-based learning methods. Cambridge University Press, 2000.
  8. [ 8] Orr, Mark J.L. (1966): Introduction to Radial Basis Function Networks, Centre for Cognitive Science, University of Edinburgh, Scotland.
  9. Ferreira, Cândida. Gene Expression Programming. Mathematical Modelling by an Artificial Intelligence, 2nd Edition. Springer-Verlag--Studies in computational intelligence 21, 2006.
  10. Hsu, C.-W and C.-J. Lin. A comparison of methods for multi-class support vector machines. IEEE Transactions on Neural Networks, 13(2):415-425, 2002.
  11. The Dempster-Shafer Theory of Belief Functions for Managing Uncertainties: An Introduction and Fraud Risk Assessment Illustration Australian Accounting Review, Volume 21, Issue 3, pp. 282–291
  12. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale NJ: Erlbaum.
  13. Islas, E., Pérez, M., Rodriguez, G., Paredes, I., Ávila, I., & Mendoza, M. (2007). E-learning tools evaluation and roadmap development for an electrical utility. Journal of Theoretical and Applied Electronic Commerce Research (JTAER), 2(1), 63–75
  14. Ahmed Younis Alsabawy, Aileen Cater-Steel, Jeffrey Soar (2013)." IT infrastructure services as a requirement for e-learning system success", Computers & Education 69 (2013) 431–451
  15. Selim, H. M. (2007). Critical success factors for e-learning acceptance: Confirmatory factor models. International Journal of Technology Marketing, 2(2), 157–182
  16. Khan, B. (2005). Managing e-learning strategies: Design, delivery, implementation and evaluation. Hershey, London, Melbourne, Singapore: Information Science Publishing
  17. Khan, B. (2001). A framework for Web-based learning. Web-based training, 75-98
  18. Shee, D. Y., & Wang, Y. S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers Education, 50(3), 894–905.
  19. Ozkan, S., Koseler, R., & Baykal, N. (2009). Evaluating learning management systems: Adoption of hexagonal e-learning assessment model in higher education. Transforming Government: People, Process and Policy, 3(2), 111–130.
  20. Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers Education, 49(4), 1066–1080.
  21. Douglas, E., & Van Der Vyver, G. (2004). Effectiveness of e-learning course materials for learning database management systems: An experimental investigation. Journal of Computer Information System, 41(4), 41–48
  22. Dr. goldi puri (2012) critical success factors in e-learning – an empirical study. international journal of multidisciplinary research.vol.2 issue 1, January 2012, issn 2231 5780
  23. Papp, R. (2000). Critical success factors for distance learning. Paper presented at the Americas Conference on Information Systems, Long Beach, CA, USA.
  24. Govindasamy, T. (2001). Successful implementation of e-Learning: Pedagogical considerations. The Internet and Higher Education, 4(3-4), 287-299.
  25. Hollsopple, C., & Lee Post, A. (2006). Defining, Assessing, and Promoting E Learning Success: An Information Systems Perspective*. Decision Sciences Journal of Innovative Education, 4(1), 67-85.
  26. Sun, P. C. ,Tsai, R. S., Finger, G., Chen, Y-Y., and Yeh, D. (2008) ‘What drives a successful eLearning? An empirical investigation of the critical factors influencing learner satisfaction. ‘Computers & Education, 50(4): 1183–1202.
  27. Webster, J., & Hackley, P. (1997). Teaching effectiveness in technology-mediated distance learning. Academy of Management Journal, 40(6), 1282–1309.
  28. Arbaugh, J. B. (2002). Managing the on-line classroom: A study of technological and behavioural characteristics of web-based MBA courses. Journal of High Technology Management Research, 3, 203–223.
  29. Konradt, U., & Sulz, K. (2001). The experience of flow in interacting with a hypermedia learning environment. Journal of Educational Multimedia and Hypermedia, 10(1), 69–84.
  30. Collis, B. (1995). Anticipating the impact of multimedia in education: Lessons from the literature. Computers in Adult Education and Training, 2(2), 136–149.
  31. Tazid Ali, Palash Dutta, and Hrishikesh Boruah. A New Combination Rule for Conflict Problem of Dempster-Shafer Evidence Theory. International Journal of Energy, Information and Communications Vol. 3, Issue 1, February, 2012
  32. Volery, T., & Lord, D. (2000). Critical success factors in online education. The International Journal of Educational Management, 14(5), 216–223.
Index Terms

Computer Science
Information Sciences

Keywords

E-learning - ranking criteria - e-learning evaluation- system Quality-Egyptian universities

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