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Using Genetic Algorithms in Pervasive Learning Systems
Current Issue
Volume 2, 2014
Issue 5 (October)
Pages: 44-47   |   Vol. 2, No. 5, October 2014   |   Follow on         
Paper in PDF Downloads: 34   Since Aug. 28, 2015 Views: 1647   Since Aug. 28, 2015
Siranush G. Sargsyan, Department of Programming and Information Technologies, Yerevan State University, Yerevan, Armenia.
Anna S. Hovakimyan, Department of Programming and Information Technologies, Yerevan State University, Yerevan, Armenia.
In this article an approach for the problem of building such tools of pervasive learning system that gives the user a chance to get the desired knowledge of teaching course in a user adaptable manner is suggested. This approach is based on “teaching scenarios” (sequence of teaching units) being constructed during the process of learning. We introduce a tool that builds user adaptable teaching scenarios based on Genetic Algorithms, built-in the Genetic Chooser Algorithm (GCA). These scenarios are being constructed via the quality and quantity characteristics of the teaching units and user’s knowledge. A course-map building tool is also introduced in GCA that helps user to see his /her progress through the course.
Genetic Algorithm, Pervasive Learning, User Adaptable Scenario, Knowledge Vector, L-Systems
S.G. Sargsyan, A.S. Hovakimyan, K.S. Darbinyan, N. Ispiryan, E. Petrosyan, TeachArm Toolset for e-learning support, Proc. of International Workshop. Information Technologies in education, Yerevan, Armenia, 2005.
A.S. Hovakimyan, S.G. Sargsyan, About Building Teaching Systems for E-Learning, Proc. of International Conf. on Advanced Learning Technologies, Kazan, Russia, 2002.
Manju Bhaskar et. al. Genetic Algorithm Based Adaptive Learning Scheme Generation For Context Aware E-Learning / (IJCSE) International Journal on Computer Science and Engineering ,Vol. 02, No. 04, 2010.
Musa, A. & Ballera, M. Personalize eLearning System using Three Parameters and Genetic Algorithms. In M. Koehler & P. Mishra (Eds.), Proceedings of Society for Information Technology & Teacher Education International Conference, 2011.
Chen, C. M., Lee, H. M., & Chen, Y. H. Personalized e-learning system using item response theory. Computers and Education, 44(3), 2005.
A.Benamar, N. Belkhatir, F. Bendimerad. Adaptive and Context-Aware Scenarios for Pervasive Technology-Enhanced Learning System. International Arab Journal of e-Technology.Vol.3, No.1, January 2013.
Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1999.
George F. Ludger, Artificial Intelligence. Structures and Strategies for Complex Problem Solving. Addison Wesley, 2003.
Davis L. Handbook of genetic algorithms. Amsterdam: Van Nostrand Reinhold. 1991.
A.S. Hovakimyan, S.G. Sargsyan, The Genetic Algorithms (GA) in Web-based Learning Systems. Proc. of IASTED International Conference on ACIT-Software Engineering (ACIT-SE 2005), Novosibirsk, Russia, 2005.
Jon McCormack, Art and the mirror of nature, Digital Creativity Volume 14, Number 1, Swets & Zeitlinger Publishers, UK, 2003
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