Using Genetic Algorithms in Pervasive Learning Systems
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