Research Article | OPEN ACCESS
Intelligent Adaptive E-learning Model for Learning Management System
S. Bhaskaran and P. Swaminathan
School of Computing, SASTRA University, India
Research Journal of Applied Sciences, Engineering and Technology 2014 16:3298-3303
Received: September 14, 2013 | Accepted: October 01, 2013 | Published: April 25, 2014
Abstract
In this study we have proposed an Intelligent Adaptive e-Learning Model that incorporates the ability to intelligently classify learners. There is a need for learning to continue, whether learners are on- or off-line. This study emphasize on developing an agent-based personalized adaptive learning model. This model is deployed as a service using agent technology and not just as an application as is the case with all other available LMS. We tested Intelligent Adaptive e-Learning Model prototype that implements an adaptive presentation of course content under conditions of intermittent Internet connections on postgraduate students studying a networking course. The study found out that it is possible for learners to study under both off-line and on-line modes through adaptive learning and the Intelligent Adaptive E-Learning system successfully classified learners and the accuracy was 85%.
Keywords:
Adaptive learning system, intelligent training systems, learning management system, software agent technology,
References
-
Bai, S.M. and S.M. Chen, 2008a. Evaluating students learning achievement using fuzzy membership functions and fuzzy rules. Expert Syst. Appl., 34(1): 399-410.
CrossRef -
Bai, S.M. and S.M. Chen, 2008b. Automatically constructing grade membership functions of fuzzy rules for students' evaluation. Expert Syst. Appl., 35(3): 1408-1414.
CrossRef -
Li, T.K. and C.M. Chen, 2009. A new method for students' learning achievement evaluation by automatically generating the weights of attributes with fuzzy reasoning capability. Proceeding of International Conference on Machine Learning Cybernetics, pp: 2834-2839.
PMCid:PMC2738128 -
Saleh, A.A., H.M. El-Bakry and T.T. Asfour, 2010. Adaptve e-learning tools for numbering systems. Proceedings of the 9th WSEAS International Conference on Telecommunications and Informatcs, pp: 293-298.
PMid:20231936 -
Tay, K.M. and C.P. Lim, 2011. A fuzzy inference system-based criterion-referenced assessment model. Expert Syst. Appl., 38(9): 11129-11136.
CrossRef -
Verdú, E., L.M. Regueras, M.J. Verdú, J.P. de Castro and M.A. Pérez, 2008. An analysis of the research on adaptive learning: The next generation of e-learning. WSEAS T. Inform. Sci. Appl., 5(6): 859-868.
-
Walker, E., N. Rummel and K.R. Koedinger, 2009. CTRL: A research framework for providing adaptive collaborative learning support. User Model. User-Adap., 19(5): 387-431.
CrossRef -
Watering, G.V.D. and J.V.D. Rijt, 2006. Teachers' and students' perceptions of assessments: A review and a study into the ability and accuracy of estimating the dif?culty levels of assessment items. Educ. Res. Rev., 1: 133-147.
CrossRef -
Wooldridge, M., 2009. An Introduction to Multi-agent Systems. John Wiley and Sons, Cambridge.
Competing interests
The authors have no competing interests.
Open Access Policy
This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
Copyright
The authors have no competing interests.
|
|
|
ISSN (Online): 2040-7467
ISSN (Print): 2040-7459 |
|
Information |
|
|
|
Sales & Services |
|
|
|